From 4761853c5c0649ee8d4d3c8e7dba1f9ab5f440c1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Wed, 8 Sep 2021 13:34:40 +0000 Subject: [PATCH 01/33] Fix imports --- TTS/tts/layers/glow_tts/encoder.py | 2 +- .../glow_tts/monotonic_align/__init__.py | 2 +- TTS/tts/layers/losses.py | 2 +- TTS/tts/layers/vits/networks.py | 2 +- TTS/tts/models/align_tts.py | 2 +- TTS/tts/models/base_tacotron.py | 2 +- TTS/tts/models/base_tts.py | 1 + TTS/tts/models/fast_pitch.py | 30 ++----------------- TTS/tts/models/glow_tts.py | 2 +- TTS/tts/models/speedy_speech.py | 2 +- TTS/tts/models/vits.py | 27 ++--------------- TTS/tts/utils/data.py | 30 ------------------- TTS/tts/utils/visual.py | 1 + TTS/utils/audio.py | 2 +- docs/source/faq.md | 2 +- docs/source/training_a_model.md | 4 +-- docs/source/what_makes_a_good_dataset.md | 1 + tests/tts_tests/test_feed_forward_layers.py | 2 +- tests/tts_tests/test_speedy_speech_layers.py | 2 +- tests/tts_tests/test_tacotron_layers.py | 2 +- 20 files changed, 24 insertions(+), 96 deletions(-) diff --git a/TTS/tts/layers/glow_tts/encoder.py b/TTS/tts/layers/glow_tts/encoder.py index f3eb4655..50bcc451 100644 --- a/TTS/tts/layers/glow_tts/encoder.py +++ b/TTS/tts/layers/glow_tts/encoder.py @@ -9,7 +9,7 @@ from TTS.tts.layers.generic.time_depth_sep_conv import TimeDepthSeparableConvBlo from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor from TTS.tts.layers.glow_tts.glow import ResidualConv1dLayerNormBlock from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask class Encoder(nn.Module): diff --git a/TTS/tts/layers/glow_tts/monotonic_align/__init__.py b/TTS/tts/layers/glow_tts/monotonic_align/__init__.py index ee058095..303467ed 100644 --- a/TTS/tts/layers/glow_tts/monotonic_align/__init__.py +++ b/TTS/tts/layers/glow_tts/monotonic_align/__init__.py @@ -2,7 +2,7 @@ import numpy as np import torch from torch.nn import functional as F -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask try: # TODO: fix pypi cython installation problem. diff --git a/TTS/tts/layers/losses.py b/TTS/tts/layers/losses.py index a2fd7635..2c752376 100644 --- a/TTS/tts/layers/losses.py +++ b/TTS/tts/layers/losses.py @@ -6,7 +6,7 @@ from coqpit import Coqpit from torch import nn from torch.nn import functional -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.ssim import ssim from TTS.utils.audio import TorchSTFT diff --git a/TTS/tts/layers/vits/networks.py b/TTS/tts/layers/vits/networks.py index cf9d6e41..cfc8b6ac 100644 --- a/TTS/tts/layers/vits/networks.py +++ b/TTS/tts/layers/vits/networks.py @@ -5,7 +5,7 @@ from torch import nn from TTS.tts.layers.glow_tts.glow import WN from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask LRELU_SLOPE = 0.1 diff --git a/TTS/tts/models/align_tts.py b/TTS/tts/models/align_tts.py index 2c3bed3d..3e5e7e5d 100644 --- a/TTS/tts/models/align_tts.py +++ b/TTS/tts/models/align_tts.py @@ -12,7 +12,7 @@ from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.pos_encoding import PositionalEncoding from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.visual import plot_alignment, plot_spectrogram from TTS.utils.audio import AudioProcessor from TTS.utils.io import load_fsspec diff --git a/TTS/tts/models/base_tacotron.py b/TTS/tts/models/base_tacotron.py index 01291775..efe379d5 100644 --- a/TTS/tts/models/base_tacotron.py +++ b/TTS/tts/models/base_tacotron.py @@ -9,7 +9,7 @@ from torch import nn from TTS.tts.layers.losses import TacotronLoss from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.speakers import SpeakerManager, get_speaker_manager from TTS.tts.utils.text import make_symbols from TTS.utils.generic_utils import format_aux_input diff --git a/TTS/tts/models/base_tts.py b/TTS/tts/models/base_tts.py index 06c7cb2b..90ac18e9 100644 --- a/TTS/tts/models/base_tts.py +++ b/TTS/tts/models/base_tts.py @@ -1,3 +1,4 @@ +from TTS.tts.utils.helpers import segment import os from typing import Dict, List, Tuple diff --git a/TTS/tts/models/fast_pitch.py b/TTS/tts/models/fast_pitch.py index 1dd0bd68..9a87fe0f 100644 --- a/TTS/tts/models/fast_pitch.py +++ b/TTS/tts/models/fast_pitch.py @@ -13,9 +13,10 @@ from TTS.tts.layers.generic.pos_encoding import PositionalEncoding from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.visual import plot_alignment, plot_pitch, plot_spectrogram from TTS.utils.audio import AudioProcessor +from TTS.tts.utils.helpers import average_over_durations @dataclass @@ -416,7 +417,7 @@ class FastPitch(BaseTTS): """ o_pitch = self.pitch_predictor(o_en, x_mask) if pitch is not None: - avg_pitch = average_pitch(pitch, dr) + avg_pitch = average_over_durations(pitch, dr) o_pitch_emb = self.pitch_emb(avg_pitch) return o_pitch_emb, o_pitch, avg_pitch o_pitch_emb = self.pitch_emb(o_pitch) @@ -670,28 +671,3 @@ class FastPitch(BaseTTS): """Enable binary alignment loss when needed""" if trainer.total_steps_done > self.config.binary_align_loss_start_step: self.use_binary_alignment_loss = True - - -def average_pitch(pitch, durs): - """Compute the average pitch value for each input character based on the durations. - - Shapes: - - pitch: :math:`[B, 1, T_de]` - - durs: :math:`[B, T_en]` - """ - - durs_cums_ends = torch.cumsum(durs, dim=1).long() - durs_cums_starts = torch.nn.functional.pad(durs_cums_ends[:, :-1], (1, 0)) - pitch_nonzero_cums = torch.nn.functional.pad(torch.cumsum(pitch != 0.0, dim=2), (1, 0)) - pitch_cums = torch.nn.functional.pad(torch.cumsum(pitch, dim=2), (1, 0)) - - bs, l = durs_cums_ends.size() - n_formants = pitch.size(1) - dcs = durs_cums_starts[:, None, :].expand(bs, n_formants, l) - dce = durs_cums_ends[:, None, :].expand(bs, n_formants, l) - - pitch_sums = (torch.gather(pitch_cums, 2, dce) - torch.gather(pitch_cums, 2, dcs)).float() - pitch_nelems = (torch.gather(pitch_nonzero_cums, 2, dce) - torch.gather(pitch_nonzero_cums, 2, dcs)).float() - - pitch_avg = torch.where(pitch_nelems == 0.0, pitch_nelems, pitch_sums / pitch_nelems) - return pitch_avg diff --git a/TTS/tts/models/glow_tts.py b/TTS/tts/models/glow_tts.py index b063b6b4..2c194a64 100644 --- a/TTS/tts/models/glow_tts.py +++ b/TTS/tts/models/glow_tts.py @@ -9,7 +9,7 @@ from TTS.tts.layers.glow_tts.decoder import Decoder from TTS.tts.layers.glow_tts.encoder import Encoder from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.speakers import get_speaker_manager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.visual import plot_alignment, plot_spectrogram diff --git a/TTS/tts/models/speedy_speech.py b/TTS/tts/models/speedy_speech.py index 86109e74..8dbf0e0a 100644 --- a/TTS/tts/models/speedy_speech.py +++ b/TTS/tts/models/speedy_speech.py @@ -10,7 +10,7 @@ from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.pos_encoding import PositionalEncoding from TTS.tts.layers.glow_tts.monotonic_align import generate_path from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.measures import alignment_diagonal_score from TTS.tts.utils.visual import plot_alignment, plot_spectrogram from TTS.utils.audio import AudioProcessor diff --git a/TTS/tts/models/vits.py b/TTS/tts/models/vits.py index 0da43f90..0aceb61c 100644 --- a/TTS/tts/models/vits.py +++ b/TTS/tts/models/vits.py @@ -14,7 +14,7 @@ from TTS.tts.layers.vits.discriminator import VitsDiscriminator from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks, TextEncoder from TTS.tts.layers.vits.stochastic_duration_predictor import StochasticDurationPredictor from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.speakers import get_speaker_manager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.visual import plot_alignment @@ -22,28 +22,7 @@ from TTS.utils.audio import AudioProcessor from TTS.utils.trainer_utils import get_optimizer, get_scheduler from TTS.vocoder.models.hifigan_generator import HifiganGenerator from TTS.vocoder.utils.generic_utils import plot_results - - -def segment(x: torch.tensor, segment_indices: torch.tensor, segment_size=4): - """Segment each sample in a batch based on the provided segment indices""" - segments = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - index_start = segment_indices[i] - index_end = index_start + segment_size - segments[i] = x[i, :, index_start:index_end] - return segments - - -def rand_segment(x: torch.tensor, x_lengths: torch.tensor = None, segment_size=4): - """Create random segments based on the input lengths.""" - B, _, T = x.size() - if x_lengths is None: - x_lengths = T - max_idxs = x_lengths - segment_size + 1 - assert all(max_idxs > 0), " [!] At least one sample is shorter than the segment size." - segment_indices = (torch.rand([B]).type_as(x) * max_idxs).long() - ret = segment(x, segment_indices, segment_size) - return ret, segment_indices +from TTS.tts.utils.helpers import rand_segments, segment @dataclass @@ -451,7 +430,7 @@ class Vits(BaseTTS): logs_p = torch.einsum("klmn, kjm -> kjn", [attn, logs_p]) # select a random feature segment for the waveform decoder - z_slice, slice_ids = rand_segment(z, y_lengths, self.spec_segment_size) + z_slice, slice_ids = rand_segments(z, y_lengths, self.spec_segment_size) o = self.waveform_decoder(z_slice, g=g) outputs.update( { diff --git a/TTS/tts/utils/data.py b/TTS/tts/utils/data.py index 887f4376..7a766958 100644 --- a/TTS/tts/utils/data.py +++ b/TTS/tts/utils/data.py @@ -54,33 +54,3 @@ def pad_per_step(inputs, pad_len): return np.pad(inputs, [[0, 0], [0, 0], [0, pad_len]], mode="constant", constant_values=0.0) -# pylint: disable=attribute-defined-outside-init -class StandardScaler: - def set_stats(self, mean, scale): - self.mean_ = mean - self.scale_ = scale - - def reset_stats(self): - delattr(self, "mean_") - delattr(self, "scale_") - - def transform(self, X): - X = np.asarray(X) - X -= self.mean_ - X /= self.scale_ - return X - - def inverse_transform(self, X): - X = np.asarray(X) - X *= self.scale_ - X += self.mean_ - return X - - -# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1 -def sequence_mask(sequence_length, max_len=None): - if max_len is None: - max_len = sequence_length.data.max() - seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device) - # B x T_max - return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1) diff --git a/TTS/tts/utils/visual.py b/TTS/tts/utils/visual.py index 7101ed3d..ff71958e 100644 --- a/TTS/tts/utils/visual.py +++ b/TTS/tts/utils/visual.py @@ -101,6 +101,7 @@ def visualize( figsize=(8, 24), output_fig=False, ): + """Intended to be used in Notebooks.""" if decoder_output is not None: num_plot = 4 diff --git a/TTS/utils/audio.py b/TTS/utils/audio.py index 01d1f7d1..a8952afc 100644 --- a/TTS/utils/audio.py +++ b/TTS/utils/audio.py @@ -9,7 +9,7 @@ import soundfile as sf import torch from torch import nn -from TTS.tts.utils.data import StandardScaler +from TTS.tts.utils.helpers import StandardScaler class TorchSTFT(nn.Module): # pylint: disable=abstract-method diff --git a/docs/source/faq.md b/docs/source/faq.md index 4dbaab13..3de6663d 100644 --- a/docs/source/faq.md +++ b/docs/source/faq.md @@ -7,7 +7,7 @@ We tried to collect common issues and questions we receive about 🐸TTS. It is - If you feel like it's a bug to be fixed, then prefer Github issues with the same level of scrutiny. ## What are the requirements of a good 🐸TTS dataset? -* https://github.com/coqui-ai/TTS/wiki/What-makes-a-good-TTS-dataset +* {ref}`See this page ` ## How should I choose the right model? - First, train Tacotron. It is smaller and faster to experiment with. If it performs poorly, try Tacotron2. diff --git a/docs/source/training_a_model.md b/docs/source/training_a_model.md index a7e81f28..aadd741e 100644 --- a/docs/source/training_a_model.md +++ b/docs/source/training_a_model.md @@ -54,7 +54,7 @@ 4. Run the training. - You need to call the python training script. + You need to run the training script. ```bash $ CUDA_VISIBLE_DEVICES="0" python train_glowtts.py @@ -63,7 +63,7 @@ Notice that you set the GPU you want to use on your system by setting `CUDA_VISIBLE_DEVICES` environment variable. To see available GPUs on your system, you can use `nvidia-smi` command on the terminal. - If you like to run a multi-gpu training + If you like to run a multi-gpu training using DDP back-end, ```bash $ CUDA_VISIBLE_DEVICES="0, 1, 2" python TTS/bin/distribute.py --script /train_glowtts.py diff --git a/docs/source/what_makes_a_good_dataset.md b/docs/source/what_makes_a_good_dataset.md index 49a2943b..18c87453 100644 --- a/docs/source/what_makes_a_good_dataset.md +++ b/docs/source/what_makes_a_good_dataset.md @@ -1,3 +1,4 @@ +(what_makes_a_good_dataset)= # What makes a good TTS dataset ## What Makes a Good Dataset diff --git a/tests/tts_tests/test_feed_forward_layers.py b/tests/tts_tests/test_feed_forward_layers.py index 1c2d3803..6b26b88f 100644 --- a/tests/tts_tests/test_feed_forward_layers.py +++ b/tests/tts_tests/test_feed_forward_layers.py @@ -2,7 +2,7 @@ import torch from TTS.tts.layers.feed_forward.decoder import Decoder from TTS.tts.layers.feed_forward.encoder import Encoder -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") diff --git a/tests/tts_tests/test_speedy_speech_layers.py b/tests/tts_tests/test_speedy_speech_layers.py index a5c481f1..adf17b8b 100644 --- a/tests/tts_tests/test_speedy_speech_layers.py +++ b/tests/tts_tests/test_speedy_speech_layers.py @@ -3,7 +3,7 @@ import torch from TTS.tts.configs import SpeedySpeechConfig from TTS.tts.layers.feed_forward.duration_predictor import DurationPredictor from TTS.tts.models.speedy_speech import SpeedySpeech, SpeedySpeechArgs -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask use_cuda = torch.cuda.is_available() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") diff --git a/tests/tts_tests/test_tacotron_layers.py b/tests/tts_tests/test_tacotron_layers.py index 783be0db..fdce75dd 100644 --- a/tests/tts_tests/test_tacotron_layers.py +++ b/tests/tts_tests/test_tacotron_layers.py @@ -4,7 +4,7 @@ import torch as T from TTS.tts.layers.losses import L1LossMasked, SSIMLoss from TTS.tts.layers.tacotron.tacotron import CBHG, Decoder, Encoder, Prenet -from TTS.tts.utils.data import sequence_mask +from TTS.tts.utils.helpers import sequence_mask # pylint: disable=unused-variable From 537c8576ecf3da788ef06488359508d06d4cc1bc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Wed, 8 Sep 2021 13:35:18 +0000 Subject: [PATCH 02/33] Stage `TTS.tts.utils.helpers` --- TTS/tts/utils/helpers.py | 111 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 111 insertions(+) create mode 100644 TTS/tts/utils/helpers.py diff --git a/TTS/tts/utils/helpers.py b/TTS/tts/utils/helpers.py new file mode 100644 index 00000000..07021786 --- /dev/null +++ b/TTS/tts/utils/helpers.py @@ -0,0 +1,111 @@ +import torch +import numpy as np + + +class StandardScaler: + """StandardScaler for mean-std normalization with the given mean and std values. + """ + def __init__(self, mean:np.ndarray=None, std:np.ndarray=None) -> None: + self.mean_ = mean + self.std_ = std + + def set_stats(self, mean, scale): + self.mean_ = mean + self.scale_ = scale + + def reset_stats(self): + delattr(self, "mean_") + delattr(self, "scale_") + + def transform(self, X): + X = np.asarray(X) + X -= self.mean_ + X /= self.scale_ + return X + + def inverse_transform(self, X): + X = np.asarray(X) + X *= self.scale_ + X += self.mean_ + return X + + +# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1 +def sequence_mask(sequence_length, max_len=None): + """Create a sequence mask for filtering padding in a sequence tensor. + + Args: + sequence_length (torch.tensor): Sequence lengths. + max_len (int, Optional): Maximum sequence length. Defaults to None. + + Shapes: + - mask: :math:`[B, T_max]` + """ + if max_len is None: + max_len = sequence_length.data.max() + seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device) + # B x T_max + mask = seq_range.unsqueeze(0) < sequence_length.unsqueeze(1) + return mask + + +def segment(x: torch.tensor, segment_indices: torch.tensor, segment_size=4): + """Segment each sample in a batch based on the provided segment indices + + Args: + x (torch.tensor): Input tensor. + segment_indices (torch.tensor): Segment indices. + segment_size (int): Expected output segment size. + """ + segments = torch.zeros_like(x[:, :, :segment_size]) + for i in range(x.size(0)): + index_start = segment_indices[i] + index_end = index_start + segment_size + segments[i] = x[i, :, index_start:index_end] + return segments + + +def rand_segments(x: torch.tensor, x_lengths: torch.tensor = None, segment_size=4): + """Create random segments based on the input lengths. + + Args: + x (torch.tensor): Input tensor. + x_lengths (torch.tensor): Input lengths. + segment_size (int): Expected output segment size. + + Shapes: + - x: :math:`[B, C, T]` + - x_lengths: :math:`[B]` + """ + B, _, T = x.size() + if x_lengths is None: + x_lengths = T + max_idxs = x_lengths - segment_size + 1 + assert all(max_idxs > 0), " [!] At least one sample is shorter than the segment size." + segment_indices = (torch.rand([B]).type_as(x) * max_idxs).long() + ret = segment(x, segment_indices, segment_size) + return ret, segment_indices + +def average_over_durations(values, durs): + """Average values over durations. + + Shapes: + - values: :math:`[B, 1, T_de]` + - durs: :math:`[B, T_en]` + - avg: :math:`[B, 1, T_en]` + """ + durs_cums_ends = torch.cumsum(durs, dim=1).long() + durs_cums_starts = torch.nn.functional.pad(durs_cums_ends[:, :-1], (1, 0)) + values_nonzero_cums = torch.nn.functional.pad(torch.cumsum(values != 0.0, dim=2), (1, 0)) + values_cums = torch.nn.functional.pad(torch.cumsum(values, dim=2), (1, 0)) + + bs, l = durs_cums_ends.size() + n_formants = values.size(1) + dcs = durs_cums_starts[:, None, :].expand(bs, n_formants, l) + dce = durs_cums_ends[:, None, :].expand(bs, n_formants, l) + + values_sums = (torch.gather(values_cums, 2, dce) - torch.gather(values_cums, 2, dcs)).float() + values_nelems = (torch.gather(values_nonzero_cums, 2, dce) - torch.gather(values_nonzero_cums, 2, dcs)).float() + + avg = torch.where(values_nelems == 0.0, values_nelems, values_sums / values_nelems) + return avg \ No newline at end of file From 807f1d38179cf1634b4c6d847edf81f2acfc9620 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Thu, 9 Sep 2021 08:59:55 +0000 Subject: [PATCH 03/33] Fix `extract_tts_spectrograms.py` model init --- TTS/bin/extract_tts_spectrograms.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/TTS/bin/extract_tts_spectrograms.py b/TTS/bin/extract_tts_spectrograms.py index 9f54cb39..112ef046 100755 --- a/TTS/bin/extract_tts_spectrograms.py +++ b/TTS/bin/extract_tts_spectrograms.py @@ -16,7 +16,6 @@ from TTS.tts.models import setup_model from TTS.tts.utils.speakers import get_speaker_manager from TTS.utils.audio import AudioProcessor from TTS.utils.generic_utils import count_parameters -from TTS.utils.io import load_fsspec use_cuda = torch.cuda.is_available() @@ -239,8 +238,7 @@ def main(args): # pylint: disable=redefined-outer-name model = setup_model(c) # restore model - checkpoint = load_fsspec(args.checkpoint_path, map_location="cpu") - model.load_state_dict(checkpoint["model"]) + model.load_checkpoint(c, args.checkpoint_path, eval=True) if use_cuda: model.cuda() From 6c4c1065b0a04296b9db38c35a49d0a63124b6c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Thu, 9 Sep 2021 09:00:27 +0000 Subject: [PATCH 04/33] Fix trainer's scheduler restoring --- TTS/trainer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/TTS/trainer.py b/TTS/trainer.py index 13439861..e5867ac9 100644 --- a/TTS/trainer.py +++ b/TTS/trainer.py @@ -275,7 +275,8 @@ class Trainer: if self.args.continue_path: if isinstance(self.scheduler, list): for scheduler in self.scheduler: - scheduler.last_epoch = self.restore_step + if scheduler is not None: + scheduler.last_epoch = self.restore_step else: self.scheduler.last_epoch = self.restore_step From abf5e48177e8bc44372e87e9a30a7890472247de Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Thu, 9 Sep 2021 09:01:04 +0000 Subject: [PATCH 05/33] Fix logging current learning rate in trainer --- TTS/trainer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/TTS/trainer.py b/TTS/trainer.py index e5867ac9..7f38ab48 100644 --- a/TTS/trainer.py +++ b/TTS/trainer.py @@ -663,6 +663,7 @@ class Trainer: lrs = {"current_lr": current_lr} # log run-time stats + loss_dict.update(lrs) loss_dict.update( { "step_time": round(step_time, 4), @@ -1126,7 +1127,7 @@ def get_last_checkpoint(path: str) -> Tuple[str, str]: last_model_num = model_num last_model = file_name - # if there is not checkpoint found above + # if there is no checkpoint found above # find the checkpoint with the latest # modification date. key_file_names = [fn for fn in file_names if key in fn] From 2dfc5bdd117c3c7d310d9e4c46c6ce889cdeda63 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Thu, 9 Sep 2021 09:01:52 +0000 Subject: [PATCH 06/33] Fix best_model_path init if no best_mode --- TTS/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/TTS/trainer.py b/TTS/trainer.py index 7f38ab48..2f3e9bb1 100644 --- a/TTS/trainer.py +++ b/TTS/trainer.py @@ -1146,7 +1146,7 @@ def get_last_checkpoint(path: str) -> Tuple[str, str]: last_models["checkpoint"] = last_models["best_model"] elif "best_model" not in last_models: # no best model # this shouldn't happen, but let's handle it just in case - last_models["best_model"] = None + last_models["best_model"] = last_models["checkpoint"] # finally check if last best model is more recent than checkpoint elif last_model_nums["best_model"] > last_model_nums["checkpoint"]: last_models["checkpoint"] = last_models["best_model"] From bfc6ceac29811201619b2b801f42d0287dc57226 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Thu, 9 Sep 2021 10:57:19 +0000 Subject: [PATCH 07/33] Move MAS to `TTS.tts.utils.helpers` --- .../glow_tts/monotonic_align/__init__.py | 106 - TTS/tts/models/align_tts.py | 2 +- TTS/tts/models/base_tacotron.py | 2 + TTS/tts/models/fast_pitch.py | 2 +- TTS/tts/models/glow_tts.py | 2 +- TTS/tts/models/speedy_speech.py | 2 +- TTS/tts/models/vits.py | 2 +- TTS/tts/utils/helpers.py | 106 +- TTS/tts/utils/monotonic_align/__init__.py | 0 TTS/tts/utils/monotonic_align/core.c | 23086 ++++++++++++++++ .../monotonic_align/core.pyx | 0 .../monotonic_align/setup.py | 0 setup.py | 4 +- 13 files changed, 23200 insertions(+), 114 deletions(-) delete mode 100644 TTS/tts/layers/glow_tts/monotonic_align/__init__.py create mode 100644 TTS/tts/utils/monotonic_align/__init__.py create mode 100644 TTS/tts/utils/monotonic_align/core.c rename TTS/tts/{layers/glow_tts => utils}/monotonic_align/core.pyx (100%) rename TTS/tts/{layers/glow_tts => utils}/monotonic_align/setup.py (100%) diff --git a/TTS/tts/layers/glow_tts/monotonic_align/__init__.py b/TTS/tts/layers/glow_tts/monotonic_align/__init__.py deleted file mode 100644 index 303467ed..00000000 --- a/TTS/tts/layers/glow_tts/monotonic_align/__init__.py +++ /dev/null @@ -1,106 +0,0 @@ -import numpy as np -import torch -from torch.nn import functional as F - -from TTS.tts.utils.helpers import sequence_mask - -try: - # TODO: fix pypi cython installation problem. - from TTS.tts.layers.glow_tts.monotonic_align.core import maximum_path_c - - CYTHON = True -except ModuleNotFoundError: - CYTHON = False - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def generate_path(duration, mask): - """ - Shapes: - - duration: :math:`[B, T_en]` - - mask: :math:'[B, T_en, T_de]` - - path: :math:`[B, T_en, T_de]` - """ - device = duration.device - b, t_x, t_y = mask.shape - cum_duration = torch.cumsum(duration, 1) - path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path * mask - return path - - -def maximum_path(value, mask): - if CYTHON: - return maximum_path_cython(value, mask) - return maximum_path_numpy(value, mask) - - -def maximum_path_cython(value, mask): - """Cython optimised version. - Shapes: - - value: :math:`[B, T_en, T_de]` - - mask: :math:`[B, T_en, T_de]` - """ - value = value * mask - device = value.device - dtype = value.dtype - value = value.data.cpu().numpy().astype(np.float32) - path = np.zeros_like(value).astype(np.int32) - mask = mask.data.cpu().numpy() - - t_x_max = mask.sum(1)[:, 0].astype(np.int32) - t_y_max = mask.sum(2)[:, 0].astype(np.int32) - maximum_path_c(path, value, t_x_max, t_y_max) - return torch.from_numpy(path).to(device=device, dtype=dtype) - - -def maximum_path_numpy(value, mask, max_neg_val=None): - """ - Monotonic alignment search algorithm - Numpy-friendly version. It's about 4 times faster than torch version. - value: [b, t_x, t_y] - mask: [b, t_x, t_y] - """ - if max_neg_val is None: - max_neg_val = -np.inf # Patch for Sphinx complaint - value = value * mask - - device = value.device - dtype = value.dtype - value = value.cpu().detach().numpy() - mask = mask.cpu().detach().numpy().astype(np.bool) - - b, t_x, t_y = value.shape - direction = np.zeros(value.shape, dtype=np.int64) - v = np.zeros((b, t_x), dtype=np.float32) - x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1) - for j in range(t_y): - v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[:, :-1] - v1 = v - max_mask = v1 >= v0 - v_max = np.where(max_mask, v1, v0) - direction[:, :, j] = max_mask - - index_mask = x_range <= j - v = np.where(index_mask, v_max + value[:, :, j], max_neg_val) - direction = np.where(mask, direction, 1) - - path = np.zeros(value.shape, dtype=np.float32) - index = mask[:, :, 0].sum(1).astype(np.int64) - 1 - index_range = np.arange(b) - for j in reversed(range(t_y)): - path[index_range, index, j] = 1 - index = index + direction[index_range, index, j] - 1 - path = path * mask.astype(np.float32) - path = torch.from_numpy(path).to(device=device, dtype=dtype) - return path diff --git a/TTS/tts/models/align_tts.py b/TTS/tts/models/align_tts.py index 3e5e7e5d..0d75f482 100644 --- a/TTS/tts/models/align_tts.py +++ b/TTS/tts/models/align_tts.py @@ -10,7 +10,7 @@ from TTS.tts.layers.feed_forward.decoder import Decoder from TTS.tts.layers.feed_forward.duration_predictor import DurationPredictor from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.pos_encoding import PositionalEncoding -from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path +from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.visual import plot_alignment, plot_spectrogram diff --git a/TTS/tts/models/base_tacotron.py b/TTS/tts/models/base_tacotron.py index efe379d5..a537afd6 100644 --- a/TTS/tts/models/base_tacotron.py +++ b/TTS/tts/models/base_tacotron.py @@ -118,9 +118,11 @@ class BaseTacotron(BaseTTS): if "r" in state: self.decoder.set_r(state["r"]) else: + # set the reduction rate from the config values embedded in the checkpoint self.decoder.set_r(state["config"]["r"]) if eval: self.eval() + print(f" > Model's reduction rate `r` is set to: {self.decoder.r}") assert not self.training def get_criterion(self) -> nn.Module: diff --git a/TTS/tts/models/fast_pitch.py b/TTS/tts/models/fast_pitch.py index 9a87fe0f..984d7d1a 100644 --- a/TTS/tts/models/fast_pitch.py +++ b/TTS/tts/models/fast_pitch.py @@ -11,7 +11,7 @@ from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.aligner import AlignmentNetwork from TTS.tts.layers.generic.pos_encoding import PositionalEncoding from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor -from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path +from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.visual import plot_alignment, plot_pitch, plot_spectrogram diff --git a/TTS/tts/models/glow_tts.py b/TTS/tts/models/glow_tts.py index 2c194a64..2f52b363 100644 --- a/TTS/tts/models/glow_tts.py +++ b/TTS/tts/models/glow_tts.py @@ -7,7 +7,7 @@ from torch.nn import functional as F from TTS.tts.configs import GlowTTSConfig from TTS.tts.layers.glow_tts.decoder import Decoder from TTS.tts.layers.glow_tts.encoder import Encoder -from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path +from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.speakers import get_speaker_manager diff --git a/TTS/tts/models/speedy_speech.py b/TTS/tts/models/speedy_speech.py index 8dbf0e0a..d85fa4dc 100644 --- a/TTS/tts/models/speedy_speech.py +++ b/TTS/tts/models/speedy_speech.py @@ -8,7 +8,7 @@ from TTS.tts.layers.feed_forward.decoder import Decoder from TTS.tts.layers.feed_forward.duration_predictor import DurationPredictor from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.pos_encoding import PositionalEncoding -from TTS.tts.layers.glow_tts.monotonic_align import generate_path +from TTS.tts.utils.helpers import generate_path from TTS.tts.models.base_tts import BaseTTS from TTS.tts.utils.helpers import sequence_mask from TTS.tts.utils.measures import alignment_diagonal_score diff --git a/TTS/tts/models/vits.py b/TTS/tts/models/vits.py index 0aceb61c..52f34e6e 100644 --- a/TTS/tts/models/vits.py +++ b/TTS/tts/models/vits.py @@ -9,7 +9,7 @@ from torch import nn from torch.cuda.amp.autocast_mode import autocast from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor -from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path +from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.layers.vits.discriminator import VitsDiscriminator from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks, TextEncoder from TTS.tts.layers.vits.stochastic_duration_predictor import StochasticDurationPredictor diff --git a/TTS/tts/utils/helpers.py b/TTS/tts/utils/helpers.py index 07021786..5ec6601a 100644 --- a/TTS/tts/utils/helpers.py +++ b/TTS/tts/utils/helpers.py @@ -1,6 +1,17 @@ import torch import numpy as np +import numpy as np +import torch +from torch.nn import functional as F + +try: + from TTS.tts.utils.monotonic_align.core import maximum_path_c + + CYTHON = True +except ModuleNotFoundError: + CYTHON = False + class StandardScaler: """StandardScaler for mean-std normalization with the given mean and std values. @@ -108,4 +119,97 @@ def average_over_durations(values, durs): values_nelems = (torch.gather(values_nonzero_cums, 2, dce) - torch.gather(values_nonzero_cums, 2, dcs)).float() avg = torch.where(values_nelems == 0.0, values_nelems, values_sums / values_nelems) - return avg \ No newline at end of file + return avg + + +def convert_pad_shape(pad_shape): + l = pad_shape[::-1] + pad_shape = [item for sublist in l for item in sublist] + return pad_shape + + +def generate_path(duration, mask): + """ + Shapes: + - duration: :math:`[B, T_en]` + - mask: :math:'[B, T_en, T_de]` + - path: :math:`[B, T_en, T_de]` + """ + device = duration.device + b, t_x, t_y = mask.shape + cum_duration = torch.cumsum(duration, 1) + path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device) + + cum_duration_flat = cum_duration.view(b * t_x) + path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) + path = path.view(b, t_x, t_y) + path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] + path = path * mask + return path + + +def maximum_path(value, mask): + if CYTHON: + return maximum_path_cython(value, mask) + return maximum_path_numpy(value, mask) + + +def maximum_path_cython(value, mask): + """Cython optimised version. + Shapes: + - value: :math:`[B, T_en, T_de]` + - mask: :math:`[B, T_en, T_de]` + """ + value = value * mask + device = value.device + dtype = value.dtype + value = value.data.cpu().numpy().astype(np.float32) + path = np.zeros_like(value).astype(np.int32) + mask = mask.data.cpu().numpy() + + t_x_max = mask.sum(1)[:, 0].astype(np.int32) + t_y_max = mask.sum(2)[:, 0].astype(np.int32) + maximum_path_c(path, value, t_x_max, t_y_max) + return torch.from_numpy(path).to(device=device, dtype=dtype) + + +def maximum_path_numpy(value, mask, max_neg_val=None): + """ + Monotonic alignment search algorithm + Numpy-friendly version. It's about 4 times faster than torch version. + value: [b, t_x, t_y] + mask: [b, t_x, t_y] + """ + if max_neg_val is None: + max_neg_val = -np.inf # Patch for Sphinx complaint + value = value * mask + + device = value.device + dtype = value.dtype + value = value.cpu().detach().numpy() + mask = mask.cpu().detach().numpy().astype(np.bool) + + b, t_x, t_y = value.shape + direction = np.zeros(value.shape, dtype=np.int64) + v = np.zeros((b, t_x), dtype=np.float32) + x_range = np.arange(t_x, dtype=np.float32).reshape(1, -1) + for j in range(t_y): + v0 = np.pad(v, [[0, 0], [1, 0]], mode="constant", constant_values=max_neg_val)[:, :-1] + v1 = v + max_mask = v1 >= v0 + v_max = np.where(max_mask, v1, v0) + direction[:, :, j] = max_mask + + index_mask = x_range <= j + v = np.where(index_mask, v_max + value[:, :, j], max_neg_val) + direction = np.where(mask, direction, 1) + + path = np.zeros(value.shape, dtype=np.float32) + index = mask[:, :, 0].sum(1).astype(np.int64) - 1 + index_range = np.arange(b) + for j in reversed(range(t_y)): + path[index_range, index, j] = 1 + index = index + direction[index_range, index, j] - 1 + path = path * mask.astype(np.float32) + path = torch.from_numpy(path).to(device=device, dtype=dtype) + return path \ No newline at end of file diff --git a/TTS/tts/utils/monotonic_align/__init__.py b/TTS/tts/utils/monotonic_align/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/TTS/tts/utils/monotonic_align/core.c b/TTS/tts/utils/monotonic_align/core.c new file mode 100644 index 00000000..5e3d619b --- /dev/null +++ b/TTS/tts/utils/monotonic_align/core.c @@ -0,0 +1,23086 @@ +/* Generated by Cython 0.29.24 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [], + "name": "TTS.tts.layers.glow_tts.monotonic_align.core", + "sources": [ + "TTS/tts/layers/glow_tts/monotonic_align/core.pyx" + ] + }, + "module_name": "TTS.tts.layers.glow_tts.monotonic_align.core" +} +END: Cython Metadata */ + +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.6+ or Python 3.3+. +#else +#define CYTHON_ABI "0_29_24" +#define CYTHON_HEX_VERSION 0x001D18F0 +#define CYTHON_FUTURE_DIVISION 1 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #elif !defined(CYTHON_USE_PYTYPE_LOOKUP) + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1) + #endif + #ifndef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3) + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) +#endif +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int32 uint32_t; + #endif + #endif +#else + #include +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) && __cplusplus >= 201103L + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #elif __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__ ) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif + +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) + #define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" +#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2 + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #if defined(PyUnicode_IS_READY) + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #else + #define __Pyx_PyUnicode_READY(op) (0) + #endif + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE) + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #endif +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__TTS__tts__layers__glow_tts__monotonic_align__core +#define __PYX_HAVE_API__TTS__tts__layers__glow_tts__monotonic_align__core +/* Early includes */ +#include +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" + + /* NumPy API declarations from "numpy/__init__.pxd" */ + +#include "pythread.h" +#include +#include "pystate.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime = NULL; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "TTS/tts/layers/glow_tts/monotonic_align/core.pyx", + "__init__.pxd", + "stringsource", + "type.pxd", +}; +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __pyx_atomic_int_type int +#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\ + !defined(__i386__) + #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0 + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type LONG + #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0 + #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value) + #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using Intel atomics" + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +typedef volatile __pyx_atomic_int_type __pyx_atomic_int; +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":689 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":690 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":691 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":692 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":696 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":697 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":698 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":699 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":703 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":704 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":713 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":714 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":715 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":717 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":718 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":719 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":721 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":722 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":724 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":725 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":726 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* Declarations.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +/* Declarations.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":728 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":729 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":730 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "../miniconda3/envs/p3.6/lib/python3.6/site-packages/numpy/__init__.pxd":732 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; +struct __pyx_opt_args_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c; + +/* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":42 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_xs, int[::1] t_ys, float max_neg_val=-1e9) nogil: # <<<<<<<<<<<<<< + * cdef int b = values.shape[0] + * + */ +struct __pyx_opt_args_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c { + int __pyx_n; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* MemviewSliceInit.proto */ +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int *acquisition_count, PyThread_type_lock lock); 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+ +/* None.proto */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif + +/* PyObjectCall2Args.proto */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* StrEquals.proto */ +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals +#else +#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals +#endif + +/* None.proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); + +/* UnaryNegOverflows.proto */ +#define UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* decode_c_string_utf16.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 0; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = -1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} +static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) { + int byteorder = 1; + return PyUnicode_DecodeUTF16(s, size, errors, &byteorder); +} + +/* decode_c_string.proto */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)); + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PyIntBinop.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); +#else +#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\ + (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2)) +#endif + +/* ListExtend.proto */ +static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject* none = _PyList_Extend((PyListObject*)L, v); + if (unlikely(!none)) + return -1; + Py_DECREF(none); + return 0; +#else + return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v); +#endif +} + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + PyList_SET_ITEM(list, len, x); + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* None.proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long); + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); + +/* PyObject_GenericGetAttrNoDict.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr +#endif + +/* PyObject_GenericGetAttr.proto */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr +#endif + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable); + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* TypeImport.proto */ +#ifndef __PYX_HAVE_RT_ImportType_proto +#define __PYX_HAVE_RT_ImportType_proto +enum __Pyx_ImportType_CheckSize { + __Pyx_ImportType_CheckSize_Error = 0, + __Pyx_ImportType_CheckSize_Warn = 1, + __Pyx_ImportType_CheckSize_Ignore = 2 +}; +static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); +#endif + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* Capsule.proto */ +static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); + +/* GCCDiagnostics.proto */ +#if defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ + +/* Module declarations from 'cython.view' */ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'cpython.mem' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; + +/* Module declarations from 'TTS.tts.layers.glow_tts.monotonic_align.core' */ +static PyTypeObject *__pyx_array_type = 0; +static PyTypeObject *__pyx_MemviewEnum_type = 0; +static PyTypeObject *__pyx_memoryview_type = 0; +static PyTypeObject *__pyx_memoryviewslice_type = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static void __pyx_f_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, float); /*proto*/ +static void __pyx_f_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch, struct __pyx_opt_args_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c *__pyx_optional_args); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ +static void *__pyx_align_pointer(void *, size_t); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 }; +static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "TTS.tts.layers.glow_tts.monotonic_align.core" +extern int __pyx_module_is_main_TTS__tts__layers__glow_tts__monotonic_align__core; +int __pyx_module_is_main_TTS__tts__layers__glow_tts__monotonic_align__core = 0; + +/* Implementation of 'TTS.tts.layers.glow_tts.monotonic_align.core' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ImportError; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_t_xs[] = "t_xs"; +static const char __pyx_k_t_ys[] = "t_ys"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_paths[] = "paths"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_values[] = "values"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_pyx_result[] = "__pyx_result"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_max_neg_val[] = "max_neg_val"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_stringsource[] = "stringsource"; +static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer"; +static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; +static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; +static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; +static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; +static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; +static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; +static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_strided_and_direct[] = ""; +static const char __pyx_k_strided_and_indirect[] = ""; +static const char __pyx_k_contiguous_and_direct[] = ""; +static const char __pyx_k_MemoryView_of_r_object[] = ""; +static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; +static const char __pyx_k_contiguous_and_indirect[] = ""; +static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'"; +static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d."; +static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; +static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; +static const char __pyx_k_strided_and_direct_or_indirect[] = ""; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; +static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; +static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; +static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; +static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; +static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))"; +static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; +static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s"; +static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)"; +static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; +static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)"; +static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; +static PyObject *__pyx_n_s_ASCII; +static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri; +static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is; +static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor; +static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi; +static PyObject *__pyx_kp_s_Cannot_index_with_type_s; +static PyObject *__pyx_n_s_Ellipsis; +static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr; +static PyObject *__pyx_n_s_ImportError; +static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0; +static PyObject *__pyx_n_s_IndexError; +static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte; +static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr; +static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d; +static PyObject *__pyx_n_s_MemoryError; +static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x; +static PyObject *__pyx_kp_s_MemoryView_of_r_object; +static PyObject *__pyx_n_b_O; +static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a; +static PyObject *__pyx_n_s_PickleError; +static PyObject *__pyx_n_s_TypeError; +static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_View_MemoryView; +static PyObject *__pyx_n_s_allocate_buffer; +static PyObject *__pyx_n_s_base; +static PyObject *__pyx_n_s_c; +static PyObject *__pyx_n_u_c; +static PyObject *__pyx_n_s_class; +static PyObject *__pyx_n_s_cline_in_traceback; +static PyObject *__pyx_kp_s_contiguous_and_direct; +static PyObject *__pyx_kp_s_contiguous_and_indirect; +static PyObject *__pyx_n_s_dict; +static PyObject *__pyx_n_s_dtype_is_object; +static PyObject *__pyx_n_s_encode; +static PyObject *__pyx_n_s_enumerate; +static PyObject *__pyx_n_s_error; +static PyObject *__pyx_n_s_flags; +static PyObject *__pyx_n_s_format; +static PyObject *__pyx_n_s_fortran; +static PyObject *__pyx_n_u_fortran; +static PyObject *__pyx_n_s_getstate; +static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi; +static PyObject *__pyx_n_s_id; +static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_itemsize; +static PyObject *__pyx_kp_s_itemsize_0_for_cython_array; +static PyObject *__pyx_n_s_main; +static PyObject *__pyx_n_s_max_neg_val; +static PyObject *__pyx_n_s_memview; +static PyObject *__pyx_n_s_mode; +static PyObject *__pyx_n_s_name; +static PyObject *__pyx_n_s_name_2; +static PyObject *__pyx_n_s_ndim; +static PyObject *__pyx_n_s_new; +static PyObject *__pyx_kp_s_no_default___reduce___due_to_non; +static PyObject *__pyx_n_s_np; +static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_kp_u_numpy_core_multiarray_failed_to; +static PyObject *__pyx_kp_u_numpy_core_umath_failed_to_impor; +static PyObject *__pyx_n_s_obj; +static PyObject *__pyx_n_s_pack; +static PyObject *__pyx_n_s_paths; +static PyObject *__pyx_n_s_pickle; +static PyObject *__pyx_n_s_pyx_PickleError; +static PyObject *__pyx_n_s_pyx_checksum; +static PyObject *__pyx_n_s_pyx_getbuffer; +static PyObject *__pyx_n_s_pyx_result; +static PyObject *__pyx_n_s_pyx_state; +static PyObject *__pyx_n_s_pyx_type; +static PyObject *__pyx_n_s_pyx_unpickle_Enum; +static PyObject *__pyx_n_s_pyx_vtable; +static PyObject *__pyx_n_s_range; +static PyObject *__pyx_n_s_reduce; +static PyObject *__pyx_n_s_reduce_cython; +static PyObject *__pyx_n_s_reduce_ex; +static PyObject *__pyx_n_s_setstate; +static PyObject *__pyx_n_s_setstate_cython; +static PyObject *__pyx_n_s_shape; +static PyObject *__pyx_n_s_size; +static PyObject *__pyx_n_s_start; +static PyObject *__pyx_n_s_step; +static PyObject *__pyx_n_s_stop; +static PyObject *__pyx_kp_s_strided_and_direct; +static PyObject *__pyx_kp_s_strided_and_direct_or_indirect; +static PyObject *__pyx_kp_s_strided_and_indirect; +static PyObject *__pyx_kp_s_stringsource; +static PyObject *__pyx_n_s_struct; +static PyObject *__pyx_n_s_t_xs; +static PyObject *__pyx_n_s_t_ys; +static PyObject *__pyx_n_s_test; +static PyObject *__pyx_kp_s_unable_to_allocate_array_data; +static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str; +static PyObject *__pyx_n_s_unpack; +static PyObject *__pyx_n_s_update; +static PyObject *__pyx_n_s_values; +static PyObject *__pyx_pf_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_xs, __Pyx_memviewslice __pyx_v_t_ys, float __pyx_v_max_neg_val); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ +static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ +static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ +static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ +static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ +static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ +static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ +static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */ +static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ +static PyObject *__pyx_int_0; +static PyObject *__pyx_int_1; +static PyObject *__pyx_int_184977713; +static PyObject *__pyx_int_neg_1; +static float __pyx_k_; +static PyObject *__pyx_tuple__2; +static PyObject *__pyx_tuple__3; +static PyObject *__pyx_tuple__4; +static PyObject *__pyx_tuple__5; +static PyObject *__pyx_tuple__6; +static PyObject *__pyx_tuple__7; +static PyObject *__pyx_tuple__8; +static PyObject *__pyx_tuple__9; +static PyObject *__pyx_slice__18; +static PyObject *__pyx_tuple__10; +static PyObject *__pyx_tuple__11; +static PyObject *__pyx_tuple__12; +static PyObject *__pyx_tuple__13; +static PyObject *__pyx_tuple__14; +static PyObject *__pyx_tuple__15; +static PyObject *__pyx_tuple__16; +static PyObject *__pyx_tuple__17; +static PyObject *__pyx_tuple__19; +static PyObject *__pyx_tuple__20; +static PyObject *__pyx_tuple__21; +static PyObject *__pyx_tuple__22; +static PyObject *__pyx_tuple__23; +static PyObject *__pyx_tuple__24; +static PyObject *__pyx_tuple__25; +static PyObject *__pyx_tuple__26; +static PyObject *__pyx_tuple__27; +static PyObject *__pyx_codeobj__28; +/* Late includes */ + +/* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":11 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_x, int t_y, float max_neg_val) nogil: # <<<<<<<<<<<<<< + * cdef int x + * cdef int y + */ + +static void __pyx_f_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice __pyx_v_path, __Pyx_memviewslice __pyx_v_value, int __pyx_v_t_x, int __pyx_v_t_y, float __pyx_v_max_neg_val) { + int __pyx_v_x; + int __pyx_v_y; + float __pyx_v_v_prev; + float __pyx_v_v_cur; + int __pyx_v_index; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + long __pyx_t_4; + int __pyx_t_5; + long __pyx_t_6; + long __pyx_t_7; + int __pyx_t_8; + Py_ssize_t __pyx_t_9; + Py_ssize_t __pyx_t_10; + float __pyx_t_11; + float __pyx_t_12; + float __pyx_t_13; + Py_ssize_t __pyx_t_14; + Py_ssize_t __pyx_t_15; + int __pyx_t_16; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":17 + * cdef float v_cur + * cdef float tmp + * cdef int index = t_x - 1 # <<<<<<<<<<<<<< + * + * for y in range(t_y): + */ + __pyx_v_index = (__pyx_v_t_x - 1); + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":19 + * cdef int index = t_x - 1 + * + * for y in range(t_y): # <<<<<<<<<<<<<< + * for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + * if x == y: + */ + __pyx_t_1 = __pyx_v_t_y; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_y = __pyx_t_3; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":20 + * + * for y in range(t_y): + * for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): # <<<<<<<<<<<<<< + * if x == y: + * v_cur = max_neg_val + */ + __pyx_t_4 = (__pyx_v_y + 1); + __pyx_t_5 = __pyx_v_t_x; + if (((__pyx_t_4 < __pyx_t_5) != 0)) { + __pyx_t_6 = __pyx_t_4; + } else { + __pyx_t_6 = __pyx_t_5; + } + __pyx_t_4 = __pyx_t_6; + __pyx_t_5 = ((__pyx_v_t_x + __pyx_v_y) - __pyx_v_t_y); + __pyx_t_6 = 0; + if (((__pyx_t_5 > __pyx_t_6) != 0)) { + __pyx_t_7 = __pyx_t_5; + } else { + __pyx_t_7 = __pyx_t_6; + } + __pyx_t_6 = __pyx_t_4; + for (__pyx_t_5 = __pyx_t_7; __pyx_t_5 < __pyx_t_6; __pyx_t_5+=1) { + __pyx_v_x = __pyx_t_5; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":21 + * for y in range(t_y): + * for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + * if x == y: # <<<<<<<<<<<<<< + * v_cur = max_neg_val + * else: + */ + __pyx_t_8 = ((__pyx_v_x == __pyx_v_y) != 0); + if (__pyx_t_8) { + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":22 + * for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + * if x == y: + * v_cur = max_neg_val # <<<<<<<<<<<<<< + * else: + * v_cur = value[x, y-1] + */ + __pyx_v_v_cur = __pyx_v_max_neg_val; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":21 + * for y in range(t_y): + * for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)): + * if x == y: # <<<<<<<<<<<<<< + * v_cur = max_neg_val + * else: + */ + goto __pyx_L7; + } + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":24 + * v_cur = max_neg_val + * else: + * v_cur = value[x, y-1] # <<<<<<<<<<<<<< + * if x == 0: + * if y == 0: + */ + /*else*/ { + __pyx_t_9 = __pyx_v_x; + __pyx_t_10 = (__pyx_v_y - 1); + __pyx_v_v_cur = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))); + } + __pyx_L7:; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":25 + * else: + * v_cur = value[x, y-1] + * if x == 0: # <<<<<<<<<<<<<< + * if y == 0: + * v_prev = 0. + */ + __pyx_t_8 = ((__pyx_v_x == 0) != 0); + if (__pyx_t_8) { + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":26 + * v_cur = value[x, y-1] + * if x == 0: + * if y == 0: # <<<<<<<<<<<<<< + * v_prev = 0. + * else: + */ + __pyx_t_8 = ((__pyx_v_y == 0) != 0); + if (__pyx_t_8) { + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":27 + * if x == 0: + * if y == 0: + * v_prev = 0. # <<<<<<<<<<<<<< + * else: + * v_prev = max_neg_val + */ + __pyx_v_v_prev = 0.; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":26 + * v_cur = value[x, y-1] + * if x == 0: + * if y == 0: # <<<<<<<<<<<<<< + * v_prev = 0. + * else: + */ + goto __pyx_L9; + } + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":29 + * v_prev = 0. + * else: + * v_prev = max_neg_val # <<<<<<<<<<<<<< + * else: + * v_prev = value[x-1, y-1] + */ + /*else*/ { + __pyx_v_v_prev = __pyx_v_max_neg_val; + } + __pyx_L9:; + + /* "TTS/tts/layers/glow_tts/monotonic_align/core.pyx":25 + * else: + * v_cur = value[x, y-1] + * if x == 0: # <<<<<<<<<<<<<< + * if 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__Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 778, __pyx_L1_error) } + + /* "View.MemoryView":779 + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * else: + */ + if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(2, 779, __pyx_L1_error) } + + /* "View.MemoryView":777 + * + * if isinstance(memview, _memoryviewslice): + * return memoryview_fromslice(dst, new_ndim, # <<<<<<<<<<<<<< + * memviewsliceobj.to_object_func, + * memviewsliceobj.to_dtype_func, + */ + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 777, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 777, __pyx_L1_error) + __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); + __pyx_t_3 = 0; + goto __pyx_L0; + + /* "View.MemoryView":776 + * new_ndim += 1 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * return memoryview_fromslice(dst, new_ndim, + * memviewsliceobj.to_object_func, + */ + } + + /* "View.MemoryView":782 + * memview.dtype_is_object) + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * + */ + /*else*/ { + __Pyx_XDECREF(((PyObject *)__pyx_r)); + + /* "View.MemoryView":783 + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(2, 782, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_3); + + /* "View.MemoryView":782 + * memview.dtype_is_object) + * else: + * return memoryview_fromslice(dst, new_ndim, NULL, NULL, # <<<<<<<<<<<<<< + * memview.dtype_is_object) + * + */ + if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(2, 782, __pyx_L1_error) + __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3); + __pyx_t_3 = 0; + goto __pyx_L0; + } + + /* "View.MemoryView":710 + * + * @cname('__pyx_memview_slice') + * cdef memoryview memview_slice(memoryview memview, object indices): # <<<<<<<<<<<<<< + * cdef int new_ndim = 0, suboffset_dim = -1, dim + * cdef bint negative_step + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_3); + __Pyx_XDECREF(__pyx_t_9); + __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj); + __Pyx_XDECREF(__pyx_v_index); + __Pyx_XGIVEREF((PyObject *)__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":807 + * + * @cname('__pyx_memoryview_slice_memviewslice') + * cdef int slice_memviewslice( # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset, + */ + +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) { + Py_ssize_t __pyx_v_new_shape; + int __pyx_v_negative_step; + int __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + + /* "View.MemoryView":827 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":829 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + __pyx_t_1 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":830 + * + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":829 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: + */ + } + + /* "View.MemoryView":831 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + __pyx_t_1 = (0 <= __pyx_v_start); + if (__pyx_t_1) { + __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); + } + __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":832 + * start += shape + * if not 0 <= start < shape: + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 832, __pyx_L1_error) + + /* "View.MemoryView":831 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) + * else: + */ + } + + /* "View.MemoryView":827 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: + */ + goto __pyx_L3; + } + + /* "View.MemoryView":835 + * else: + * + * negative_step = have_step != 0 and step < 0 # <<<<<<<<<<<<<< + * + * if have_step and step == 0: + */ + /*else*/ { + __pyx_t_1 = ((__pyx_v_have_step != 0) != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L6_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step < 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L6_bool_binop_done:; + __pyx_v_negative_step = __pyx_t_2; + + /* "View.MemoryView":837 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + __pyx_t_1 = (__pyx_v_have_step != 0); + if (__pyx_t_1) { + } else { + __pyx_t_2 = __pyx_t_1; + goto __pyx_L9_bool_binop_done; + } + __pyx_t_1 = ((__pyx_v_step == 0) != 0); + __pyx_t_2 = __pyx_t_1; + __pyx_L9_bool_binop_done:; + if (__pyx_t_2) { + + /* "View.MemoryView":838 + * + * if have_step and step == 0: + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(2, 838, __pyx_L1_error) + + /* "View.MemoryView":837 + * negative_step = have_step != 0 and step < 0 + * + * if have_step and step == 0: # <<<<<<<<<<<<<< + * _err_dim(ValueError, "Step may not be zero (axis %d)", dim) + * + */ + } + + /* "View.MemoryView":841 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":842 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":843 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 + */ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":844 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + __pyx_t_2 = ((__pyx_v_start < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":845 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: + */ + __pyx_v_start = 0; + + /* "View.MemoryView":844 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: + */ + } + + /* "View.MemoryView":842 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: + */ + goto __pyx_L12; + } + + /* "View.MemoryView":846 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":847 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":847 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L14; + } + + /* "View.MemoryView":850 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L14:; + + /* "View.MemoryView":846 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 + */ + } + __pyx_L12:; + + /* "View.MemoryView":841 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape + */ + goto __pyx_L11; + } + + /* "View.MemoryView":852 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":853 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 + */ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":852 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: + */ + goto __pyx_L15; + } + + /* "View.MemoryView":855 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: + */ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L15:; + } + __pyx_L11:; + + /* "View.MemoryView":857 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":858 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":859 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 + */ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":860 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + __pyx_t_2 = ((__pyx_v_stop < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":861 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape + */ + __pyx_v_stop = 0; + + /* "View.MemoryView":860 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: + */ + } + + /* "View.MemoryView":858 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: + */ + goto __pyx_L17; + } + + /* "View.MemoryView":862 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":863 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: + */ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":862 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: + */ + } + __pyx_L17:; + + /* "View.MemoryView":857 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape + */ + goto __pyx_L16; + } + + /* "View.MemoryView":865 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + /*else*/ { + __pyx_t_2 = (__pyx_v_negative_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":866 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape + */ + __pyx_v_stop = -1L; + + /* "View.MemoryView":865 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: + */ + goto __pyx_L19; + } + + /* "View.MemoryView":868 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * if not have_step: + */ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L19:; + } + __pyx_L16:; + + /* "View.MemoryView":870 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":871 + * + * if not have_step: + * step = 1 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_step = 1; + + /* "View.MemoryView":870 + * stop = shape + * + * if not have_step: # <<<<<<<<<<<<<< + * step = 1 + * + */ + } + + /* "View.MemoryView":875 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: + */ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":877 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":878 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: + */ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":877 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * + */ + } + + /* "View.MemoryView":880 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":881 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * + */ + __pyx_v_new_shape = 0; + + /* "View.MemoryView":880 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * + */ + } + + /* "View.MemoryView":884 + * + * + * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset + */ + (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); + + /* 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(*to_dtype_func)(char *, object) except 0 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + */ + __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); + __pyx_t_2 = (__pyx_t_1 != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1095 + * + * if isinstance(memview, _memoryviewslice): + * to_object_func = (<_memoryviewslice> memview).to_object_func # <<<<<<<<<<<<<< + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + */ + __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func; + __pyx_v_to_object_func = __pyx_t_3; + + /* "View.MemoryView":1096 + * if isinstance(memview, _memoryviewslice): + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func # <<<<<<<<<<<<<< + * else: + * to_object_func = NULL + */ + __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func; + __pyx_v_to_dtype_func = __pyx_t_4; + + /* "View.MemoryView":1094 + * cdef int (*to_dtype_func)(char *, object) except 0 + * + * if isinstance(memview, _memoryviewslice): # <<<<<<<<<<<<<< + * to_object_func = (<_memoryviewslice> memview).to_object_func + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + */ + goto __pyx_L3; + } + + /* "View.MemoryView":1098 + * to_dtype_func = (<_memoryviewslice> memview).to_dtype_func + * else: + * to_object_func = NULL # <<<<<<<<<<<<<< + * to_dtype_func = NULL + * + */ + /*else*/ { + __pyx_v_to_object_func = NULL; + + /* "View.MemoryView":1099 + * else: + * to_object_func = NULL + * to_dtype_func = NULL # <<<<<<<<<<<<<< + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + */ + __pyx_v_to_dtype_func = NULL; + } + __pyx_L3:; + + /* "View.MemoryView":1101 + * to_dtype_func = NULL + * + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, # <<<<<<<<<<<<<< + * to_object_func, to_dtype_func, + * memview.dtype_is_object) + */ + __Pyx_XDECREF(__pyx_r); + + /* "View.MemoryView":1103 + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + * to_object_func, to_dtype_func, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) __PYX_ERR(2, 1101, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_5); + __pyx_r = __pyx_t_5; + __pyx_t_5 = 0; + goto __pyx_L0; + + /* "View.MemoryView":1087 + * + * @cname('__pyx_memoryview_copy_object_from_slice') + * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): # <<<<<<<<<<<<<< + * """ + * Create a new memoryview object from a given memoryview object and slice. + */ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_5); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + +static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { + Py_ssize_t __pyx_r; + int __pyx_t_1; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + __pyx_t_1 = ((__pyx_v_arg < 0) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1111 + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: + * return -arg # <<<<<<<<<<<<<< + * else: + * return arg + */ + __pyx_r = (-__pyx_v_arg); + goto __pyx_L0; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: + * if arg < 0: # <<<<<<<<<<<<<< + * return -arg + * else: + */ + } + + /* "View.MemoryView":1113 + * return -arg + * else: + * return arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') + */ + /*else*/ { + __pyx_r = __pyx_v_arg; + goto __pyx_L0; + } + + /* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil: # <<<<<<<<<<<<<< + * if arg < 0: + * return -arg + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1116 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1121 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * + */ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1122 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): + */ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1124 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + */ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1125 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1126 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1127 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): + */ + goto __pyx_L4_break; + + /* "View.MemoryView":1125 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1129 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + */ + __pyx_t_1 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_1; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1130 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1131 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * + */ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1132 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + */ + goto __pyx_L7_break; + + /* "View.MemoryView":1130 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break + */ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1134 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1135 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' + */ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1134 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: + */ + } + + /* "View.MemoryView":1137 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) + */ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1116 + * + * @cname('__pyx_get_best_slice_order') + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< + * """ + * Figure out the best memory access order for a given slice. + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1140 + * + * @cython.cdivision(True) + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< + * char *dst_data, Py_ssize_t *dst_strides, + * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, + */ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + Py_ssize_t __pyx_t_6; + + /* "View.MemoryView":1147 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + */ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1148 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] + */ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + */ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1150 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: + */ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1152 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + */ + __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":1153 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1154 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + */ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_3 = (__pyx_t_2 != 0); + __pyx_t_1 = __pyx_t_3; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1153 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + if (__pyx_t_1) { + + /* "View.MemoryView":1155 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1153 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) + */ + goto __pyx_L4; + } + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + */ + /*else*/ { + __pyx_t_4 = __pyx_v_dst_extent; + __pyx_t_5 = __pyx_t_4; + for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { + __pyx_v_i = __pyx_t_6; + + /* "View.MemoryView":1158 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride + */ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1159 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: + */ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1160 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): + */ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1152 + * cdef 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"Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + Py_ssize_t *__pyx_t_2; + Py_ssize_t *__pyx_t_3; + Py_ssize_t *__pyx_t_4; + + /* "View.MemoryView":1179 + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for shape in src.shape[:ndim]: + */ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1181 + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + * + * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< + * size *= shape + * + */ + __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); + for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { + __pyx_t_2 = __pyx_t_4; + __pyx_v_shape = (__pyx_t_2[0]); + + /* "View.MemoryView":1182 + * + * for shape in src.shape[:ndim]: + * size *= shape # <<<<<<<<<<<<<< + * + * return size + */ + __pyx_v_size = (__pyx_v_size * __pyx_v_shape); + } + + /* "View.MemoryView":1184 + * size *= shape + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') + */ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1177 + * + * @cname('__pyx_memoryview_slice_get_size') + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + */ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1187 + * + * @cname('__pyx_fill_contig_strides_array') + * 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slice_is_contig(dst, 'F', ndim) + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); + + /* "View.MemoryView":1313 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + */ + goto __pyx_L12; + } + + /* "View.MemoryView":1315 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1316 + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) # <<<<<<<<<<<<<< + * + * if direct_copy: + */ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim); + + /* "View.MemoryView":1315 + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + */ + } + __pyx_L12:; + + /* "View.MemoryView":1318 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + __pyx_t_2 = (__pyx_v_direct_copy != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":1320 + * if direct_copy: + * + * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1321 + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + */ + (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim))); + + /* "View.MemoryView":1322 + * refcount_copying(&dst, dtype_is_object, ndim, False) + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< + * free(tmpdata) + * return 0 + */ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1323 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * + */ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1324 + * refcount_copying(&dst, dtype_is_object, ndim, True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): + */ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1318 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, False) + */ + } + + /* "View.MemoryView":1310 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * + */ + } + + /* "View.MemoryView":1326 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * + */ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + __pyx_t_8 = (__pyx_t_2 != 0); + if (__pyx_t_8) { + + /* "View.MemoryView":1329 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * + */ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(2, 1329, __pyx_L1_error) + + /* "View.MemoryView":1330 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * 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(*Py_TYPE(o)->tp_free)(o); +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0}, + {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0}, + {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0}, + {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0}, + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, + {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, + {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, + {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, + {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, + {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, + {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, + {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, + {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getreadbuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getwritebuffer*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getsegcount*/ + #endif + #if PY_MAJOR_VERSION < 3 + 0, /*bf_getcharbuffer*/ + #endif + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "TTS.tts.layers.glow_tts.monotonic_align.core.memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 + 0, /*tp_vectorcall*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 + 0, /*tp_print*/ + #endif +}; +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XDEC_MEMVIEW(&p->from_slice, 1); + return 0; +} + +static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o); +} + +static PyMethodDef __pyx_methods__memoryviewslice[] = { + {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0}, + {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = { + {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0}, + {0, 0, 0, 0, 0} +}; + +static PyTypeObject __pyx_type___pyx_memoryviewslice = { + PyVarObject_HEAD_INIT(0, 0) + "TTS.tts.layers.glow_tts.monotonic_align.core._memoryviewslice", /*tp_name*/ + sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + #if PY_MAJOR_VERSION < 3 + 0, /*tp_compare*/ + #endif + #if PY_MAJOR_VERSION >= 3 + 0, /*tp_as_async*/ + #endif + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___repr__, /*tp_repr*/ + #else + 0, /*tp_repr*/ + #endif + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + #if CYTHON_COMPILING_IN_PYPY + __pyx_memoryview___str__, /*tp_str*/ + #else + 0, /*tp_str*/ + #endif + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + "Internal class for passing memoryview slices to Python", /*tp_doc*/ + __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/ + __pyx_tp_clear__memoryviewslice, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods__memoryviewslice, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets__memoryviewslice, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + 0, /*tp_dictoffset*/ + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new__memoryviewslice, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if PY_VERSION_HEX >= 0x030400a1 + 0, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 + 0, /*tp_vectorcall*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000 + 0, /*tp_print*/ + #endif +}; + +static PyMethodDef __pyx_methods[] = { + {"maximum_path_c", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_3TTS_3tts_6layers_8glow_tts_15monotonic_align_4core_1maximum_path_c, METH_VARARGS|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +#if PY_MAJOR_VERSION >= 3 +#if CYTHON_PEP489_MULTI_PHASE_INIT +static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/ +static int __pyx_pymod_exec_core(PyObject* module); /*proto*/ +static PyModuleDef_Slot __pyx_moduledef_slots[] = { + {Py_mod_create, (void*)__pyx_pymod_create}, + {Py_mod_exec, (void*)__pyx_pymod_exec_core}, + {0, NULL} +}; +#endif + +static struct PyModuleDef __pyx_moduledef = { + PyModuleDef_HEAD_INIT, + "core", + 0, /* m_doc */ + #if CYTHON_PEP489_MULTI_PHASE_INIT + 0, /* m_size */ + #else + -1, /* m_size */ + #endif + __pyx_methods /* m_methods */, + #if CYTHON_PEP489_MULTI_PHASE_INIT + 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Py_NO_RETURN { + va_list vargs; + char msg[200]; +#ifdef HAVE_STDARG_PROTOTYPES + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + int first_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) + return; + if (unlikely(__pyx_get_slice_count(memview) < 0)) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + first_time = __pyx_add_acquisition_count(memview) == 0; + if (unlikely(first_time)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } +} +static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + int last_time; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + if (unlikely(__pyx_get_slice_count(memview) <= 0)) + __pyx_fatalerror("Acquisition count is %d (line %d)", + __pyx_get_slice_count(memview), lineno); + last_time = __pyx_sub_acquisition_count(memview) == 1; + memslice->data = NULL; + if (unlikely(last_time)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + memslice->memview = NULL; + } +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* None */ +static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + #endif + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; icurexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + if (unlikely(PyTuple_Check(err))) + return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = Py_TYPE(func)->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* RaiseException */ +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* PyCFunctionFastCall */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); + } +} +#endif + +/* PyFunctionFastCall */ +#if CYTHON_FAST_PYCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyObjectCall2Args */ +static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { + PyObject *args, *result = NULL; + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyFunction_FastCall(function, args, 2); + } + #endif + #if CYTHON_FAST_PYCCALL + if (__Pyx_PyFastCFunction_Check(function)) { + PyObject *args[2] = {arg1, arg2}; + return __Pyx_PyCFunction_FastCall(function, args, 2); + } + #endif + args = PyTuple_New(2); + if (unlikely(!args)) goto done; + Py_INCREF(arg1); + PyTuple_SET_ITEM(args, 0, arg1); + Py_INCREF(arg2); + PyTuple_SET_ITEM(args, 1, arg2); + Py_INCREF(function); + result = __Pyx_PyObject_Call(function, args, NULL); + Py_DECREF(args); + Py_DECREF(function); +done: + return result; +} + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (__Pyx_PyFastCFunction_Check(func)) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY + return PyObject_RichCompareBool(s1, s2, equals); +#else +#if PY_MAJOR_VERSION < 3 + PyObject* owned_ref = NULL; +#endif + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); +#if PY_MAJOR_VERSION < 3 + if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { + owned_ref = PyUnicode_FromObject(s2); + if (unlikely(!owned_ref)) + return -1; + s2 = owned_ref; + s2_is_unicode = 1; + } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { + owned_ref = PyUnicode_FromObject(s1); + if (unlikely(!owned_ref)) + return -1; + s1 = owned_ref; + s1_is_unicode = 1; + } else if (((!s2_is_unicode) & (!s1_is_unicode))) { + return __Pyx_PyBytes_Equals(s1, s2, equals); + } +#endif + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length; + int kind; + void *data1, *data2; + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + length = __Pyx_PyUnicode_GET_LENGTH(s1); + if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + #if CYTHON_PEP393_ENABLED + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + #else + hash1 = ((PyUnicodeObject*)s1)->hash; + hash2 = ((PyUnicodeObject*)s2)->hash; + #endif + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_EQ); +return_ne: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(owned_ref); + #endif + return (equals == Py_NE); +#endif +} + +/* None */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS +#if PY_MAJOR_VERSION >= 3 + if (likely(PyUnicode_Check(n))) +#else + if (likely(PyString_Check(n))) +#endif + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (!j) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + PyObject *r = PyList_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return m->sq_item(o, i); + } + } +#else + if (is_list || PySequence_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) { + PyObject *runerr; + Py_ssize_t key_value; + PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence; + if (unlikely(!(m && m->sq_item))) { + PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name); + return NULL; + } + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) { + PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping; + if (likely(m && m->mp_subscript)) { + return m->mp_subscript(obj, key); + } + return __Pyx_PyObject_GetIndex(obj, key); +} +#endif + +/* decode_c_string */ +static CYTHON_INLINE PyObject* __Pyx_decode_c_string( + const char* cstring, Py_ssize_t start, Py_ssize_t stop, + const char* encoding, const char* errors, + PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { + Py_ssize_t length; + if (unlikely((start < 0) | (stop < 0))) { + size_t slen = strlen(cstring); + if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, + "c-string too long to convert to Python"); + return NULL; + } + length = (Py_ssize_t) slen; + if (start < 0) { + start += length; + if (start < 0) + start = 0; + } + if (stop < 0) + stop += length; + } + if (unlikely(stop <= start)) + return __Pyx_NewRef(__pyx_empty_unicode); + length = stop - start; + cstring += start; + if (decode_func) { + return decode_func(cstring, length, errors); + } else { + return PyUnicode_Decode(cstring, length, encoding, errors); + } +} + +/* GetAttr3 */ +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r = __Pyx_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; i= 0 || (x^b) >= 0)) + return PyInt_FromLong(x); + return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + #endif + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(PyLong_CheckExact(op1))) { + const long b = intval; + long a, x; +#ifdef HAVE_LONG_LONG + const PY_LONG_LONG llb = intval; + PY_LONG_LONG lla, llx; +#endif + const digit* digits = ((PyLongObject*)op1)->ob_digit; + const Py_ssize_t size = Py_SIZE(op1); + if (likely(__Pyx_sst_abs(size) <= 1)) { + a = likely(size) ? digits[0] : 0; + if (size == -1) a = -a; + } else { + switch (size) { + case -2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 2: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 3: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case -4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + case 4: + if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])); + break; +#ifdef HAVE_LONG_LONG + } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) { + lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0])); + goto long_long; +#endif + } + CYTHON_FALLTHROUGH; + default: return PyLong_Type.tp_as_number->nb_add(op1, op2); + } + } + x = a + b; + return PyLong_FromLong(x); +#ifdef HAVE_LONG_LONG + long_long: + llx = lla + llb; + return PyLong_FromLongLong(llx); +#endif + + + } + #endif + if (PyFloat_CheckExact(op1)) { + const long b = intval; + double a = PyFloat_AS_DOUBLE(op1); + double result; + PyFPE_START_PROTECT("add", return NULL) + result = ((double)a) + (double)b; + PyFPE_END_PROTECT(result) + return PyFloat_FromDouble(result); + } + return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2); +} +#endif + +/* None */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b) { + long q = a / b; + long r = a - q*b; + q -= ((r != 0) & ((r ^ b) < 0)); + return q; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Format(PyExc_ImportError, + #if PY_MAJOR_VERSION < 3 + "cannot import name %.230s", PyString_AS_STRING(name)); + #else + "cannot import name %S", name); + #endif + } + return value; +} + +/* HasAttr */ +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!__Pyx_PyBaseString_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_GetAttr(o, n); + if (unlikely(!r)) { + PyErr_Clear(); + return 0; + } else { + Py_DECREF(r); + return 1; + } +} + +/* PyObject_GenericGetAttrNoDict */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { + PyErr_Format(PyExc_AttributeError, +#if PY_MAJOR_VERSION >= 3 + "'%.50s' object has no attribute '%U'", + tp->tp_name, attr_name); +#else + "'%.50s' object has no attribute '%.400s'", + tp->tp_name, PyString_AS_STRING(attr_name)); +#endif + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { + PyObject *descr; + PyTypeObject *tp = Py_TYPE(obj); + if (unlikely(!PyString_Check(attr_name))) { + return PyObject_GenericGetAttr(obj, attr_name); + } + assert(!tp->tp_dictoffset); + descr = _PyType_Lookup(tp, attr_name); + if (unlikely(!descr)) { + return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); + } + Py_INCREF(descr); + #if PY_MAJOR_VERSION < 3 + if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) + #endif + { + descrgetfunc f = Py_TYPE(descr)->tp_descr_get; + if (unlikely(f)) { + PyObject *res = f(descr, obj, (PyObject *)tp); + Py_DECREF(descr); + return res; + } + } + return descr; +} +#endif + +/* PyObject_GenericGetAttr */ +#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 +static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { + if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { + return PyObject_GenericGetAttr(obj, attr_name); + } + return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); +} +#endif + +/* SetVTable */ +static int __Pyx_SetVtable(PyObject *dict, void *vtable) { +#if PY_VERSION_HEX >= 0x02070000 + PyObject *ob = PyCapsule_New(vtable, 0, 0); +#else + PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); +#endif + if (!ob) + goto bad; + if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* PyObjectGetAttrStrNoError */ +static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + __Pyx_PyErr_Clear(); +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1 + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { + return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); + } +#endif + result = __Pyx_PyObject_GetAttrStr(obj, attr_name); + if (unlikely(!result)) { + __Pyx_PyObject_GetAttrStr_ClearAttributeError(); + } + return result; +} + +/* SetupReduce */ +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; +#else + if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD; +#endif +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); + if (likely(reduce_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); + if (likely(setstate_cython)) { + ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) + PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name); + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* TypeImport */ +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, + size_t size, enum __Pyx_ImportType_CheckSize check_size) +{ + PyObject *result = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + result = PyObject_GetAttrString(module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if ((size_t)basicsize < size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + goto bad; + } + if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + goto bad; + } + else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(result); + return NULL; +} +#endif + +/* CLineInTraceback */ +#ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); + if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* Capsule */ +static CYTHON_INLINE PyObject * +__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) +{ + PyObject *cobj; +#if PY_VERSION_HEX >= 0x02070000 + cobj = PyCapsule_New(p, sig, NULL); +#else + cobj = PyCObject_FromVoidPtr(p, NULL); +#endif + return cobj; +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + __Pyx_StructField *field_a = a->fields + i; + __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) +{ + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + } + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF(new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, + &__Pyx_TypeInfo_float, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, + &__Pyx_TypeInfo_int, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = (float)(1.0) / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = (float)(1.0) / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = (double)(1.0) / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = (double)(1.0) / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { + PyTuple_SET_ITEM(shape_tuple, i, temp_int); + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF(new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF(array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(char) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (char) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case 1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0]) + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(char) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (char) 0; + case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(char, digit, +digits[0]) + case -2: + if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(char) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(char) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(char) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } +#endif + if (sizeof(char) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + char val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (char) -1; + } + } else { + char val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (char) -1; + val = __Pyx_PyInt_As_char(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/TTS/tts/layers/glow_tts/monotonic_align/core.pyx b/TTS/tts/utils/monotonic_align/core.pyx similarity index 100% rename from TTS/tts/layers/glow_tts/monotonic_align/core.pyx rename to TTS/tts/utils/monotonic_align/core.pyx diff --git a/TTS/tts/layers/glow_tts/monotonic_align/setup.py b/TTS/tts/utils/monotonic_align/setup.py similarity index 100% rename from TTS/tts/layers/glow_tts/monotonic_align/setup.py rename to TTS/tts/utils/monotonic_align/setup.py diff --git a/setup.py b/setup.py index bd6a6aae..53abe468 100644 --- a/setup.py +++ b/setup.py @@ -54,8 +54,8 @@ with open("README.md", "r", encoding="utf-8") as readme_file: exts = [ Extension( - name="TTS.tts.layers.glow_tts.monotonic_align.core", - sources=["TTS/tts/layers/glow_tts/monotonic_align/core.pyx"], + name="TTS.tts.utils.monotonic_align.core", + sources=["TTS/tts/utils/monotonic_align/core.pyx"], ) ] setup( From 1de010acd4881b18a881b905264964e05eb98d66 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Thu, 9 Sep 2021 10:59:23 +0000 Subject: [PATCH 08/33] Update notebook compat --- notebooks/ExtractTTSpectrogram.ipynb | 170 ++++++++++++++------------- 1 file changed, 90 insertions(+), 80 deletions(-) diff --git a/notebooks/ExtractTTSpectrogram.ipynb b/notebooks/ExtractTTSpectrogram.ipynb index 4e42a3bb..3597ebe3 100644 --- a/notebooks/ExtractTTSpectrogram.ipynb +++ b/notebooks/ExtractTTSpectrogram.ipynb @@ -2,16 +2,14 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, "source": [ - "This is a notebook to generate mel-spectrograms from a TTS model to be used for WaveRNN training." - ] + "This is a notebook to generate mel-spectrograms from a TTS model to be used in a Vocoder training." + ], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -25,22 +23,23 @@ "from TTS.tts.datasets.TTSDataset import TTSDataset\n", "from TTS.tts.layers.losses import L1LossMasked\n", "from TTS.utils.audio import AudioProcessor\n", - "from TTS.utils.io import load_config\n", + "from TTS.config import load_config\n", "from TTS.tts.utils.visual import plot_spectrogram\n", - "from TTS.tts.utils.generic_utils import setup_model, sequence_mask\n", + "from TTS.tts.utils.helpers import sequence_mask\n", + "from TTS.tts.models import setup_model\n", "from TTS.tts.utils.text.symbols import make_symbols, symbols, phonemes\n", "\n", "%matplotlib inline\n", "\n", "import os\n", - "os.environ['CUDA_VISIBLE_DEVICES']='0'" - ] + "os.environ['CUDA_VISIBLE_DEVICES']='2'" + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "def set_filename(wav_path, out_path):\n", " wav_file = os.path.basename(wav_path)\n", @@ -52,20 +51,20 @@ " mel_path = os.path.join(out_path, \"mel\", file_name)\n", " wav_path = os.path.join(out_path, \"wav_gl\", file_name)\n", " return file_name, wavq_path, mel_path, wav_path" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ - "OUT_PATH = \"/home/erogol/gdrive/Datasets/non-binary-voice-files/tacotron-DCA\"\n", - "DATA_PATH = \"/home/erogol/gdrive/Datasets/non-binary-voice-files/\"\n", - "DATASET = \"sam_accenture\"\n", - "METADATA_FILE = \"recording_script.xml\"\n", - "CONFIG_PATH = \"/home/erogol/gdrive/Trainings/sam/ljspeech-dcattn-April-03-2021_05+02-2344379/config.json\"\n", - "MODEL_FILE = \"/home/erogol/gdrive/Trainings/sam/ljspeech-dcattn-April-03-2021_05+02-2344379/best_model.pth.tar\"\n", + "OUT_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/specs2/\"\n", + "DATA_PATH = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/\"\n", + "DATASET = \"ljspeech\"\n", + "METADATA_FILE = \"metadata.csv\"\n", + "CONFIG_PATH = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/config.json\"\n", + "MODEL_FILE = \"/home/ubuntu/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC_ph/model_file.pth.tar\"\n", "BATCH_SIZE = 32\n", "\n", "QUANTIZED_WAV = False\n", @@ -78,56 +77,63 @@ "C = load_config(CONFIG_PATH)\n", "C.audio['do_trim_silence'] = False # IMPORTANT!!!!!!!!!!!!!!! disable to align mel specs with the wav files\n", "ap = AudioProcessor(bits=QUANTIZE_BIT, **C.audio)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ + "print(C['r'])\n", "# if the vocabulary was passed, replace the default\n", - "if 'characters' in C.keys():\n", + "if 'characters' in C and C['characters']:\n", " symbols, phonemes = make_symbols(**C.characters)\n", "\n", "# load the model\n", "num_chars = len(phonemes) if C.use_phonemes else len(symbols)\n", "# TODO: multiple speaker\n", - "model = setup_model(num_chars, num_speakers=0, c=C)\n", - "checkpoint = torch.load(MODEL_FILE)\n", - "model.load_state_dict(checkpoint['model'])\n", - "print(checkpoint['step'])\n", - "model.eval()\n", - "model.decoder.set_r(checkpoint['r'])\n", - "if use_cuda:\n", - " model = model.cuda()" - ] + "model = setup_model(C)\n", + "model.load_checkpoint(C, MODEL_FILE, eval=True)" + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ - "preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n", + "preprocessor = importlib.import_module(\"TTS.tts.datasets.formatters\")\n", "preprocessor = getattr(preprocessor, DATASET.lower())\n", - "meta_data = preprocessor(DATA_PATH,METADATA_FILE)\n", - "dataset = TTSDataset(checkpoint['r'], C.text_cleaner, False, ap, meta_data,characters=c.characters if 'characters' in C.keys() else None, use_phonemes=C.use_phonemes, phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n", - "loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)" - ] + "meta_data = preprocessor(DATA_PATH, METADATA_FILE)\n", + "dataset = TTSDataset(\n", + " checkpoint[\"config\"][\"r\"],\n", + " C.text_cleaner,\n", + " False,\n", + " ap,\n", + " meta_data,\n", + " characters=C.get('characters', None),\n", + " use_phonemes=C.use_phonemes,\n", + " phoneme_cache_path=C.phoneme_cache_path,\n", + " enable_eos_bos=C.enable_eos_bos_chars,\n", + ")\n", + "loader = DataLoader(\n", + " dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False\n", + ")\n" + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Generate model outputs " - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "import pickle\n", "\n", @@ -206,42 +212,42 @@ "\n", " print(np.mean(losses))\n", " print(np.mean(postnet_losses))" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# for pwgan\n", "with open(os.path.join(OUT_PATH, \"metadata.txt\"), \"w\") as f:\n", " for data in metadata:\n", " f.write(f\"{data[0]}|{data[1]+'.npy'}\\n\")" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "metadata": {}, "source": [ "### Sanity Check" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "idx = 1\n", "ap.melspectrogram(ap.load_wav(item_idx[idx])).shape" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "import soundfile as sf\n", "wav, sr = sf.read(item_idx[idx])\n", @@ -249,46 +255,46 @@ "mel_decoder = mel_outputs[idx][:mel_lengths[idx], :].detach().cpu().numpy()\n", "mel_truth = ap.melspectrogram(wav)\n", "print(mel_truth.shape)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# plot posnet output\n", "print(mel_postnet[:mel_lengths[idx], :].shape)\n", "plot_spectrogram(mel_postnet, ap)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# plot decoder output\n", "print(mel_decoder.shape)\n", "plot_spectrogram(mel_decoder, ap)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# plot GT specgrogram\n", "print(mel_truth.shape)\n", "plot_spectrogram(mel_truth.T, ap)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# postnet, decoder diff\n", "from matplotlib import pylab as plt\n", @@ -297,13 +303,13 @@ "plt.imshow(abs(mel_diff[:mel_lengths[idx],:]).T,aspect=\"auto\", origin=\"lower\");\n", "plt.colorbar()\n", "plt.tight_layout()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# PLOT GT SPECTROGRAM diff\n", "from matplotlib import pylab as plt\n", @@ -312,13 +318,13 @@ "plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n", "plt.colorbar()\n", "plt.tight_layout()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, - "outputs": [], "source": [ "# PLOT GT SPECTROGRAM diff\n", "from matplotlib import pylab as plt\n", @@ -328,21 +334,22 @@ "plt.imshow(abs(mel_diff2).T,aspect=\"auto\", origin=\"lower\");\n", "plt.colorbar()\n", "plt.tight_layout()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "source": [], "outputs": [], - "source": [] + "metadata": {} } ], "metadata": { "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "name": "python3", + "display_name": "Python 3.9.7 64-bit ('base': conda)" }, "language_info": { "codemirror_mode": { @@ -354,7 +361,10 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" + }, + "interpreter": { + "hash": "822ce188d9bce5372c4adbb11364eeb49293228c2224eb55307f4664778e7f56" } }, "nbformat": 4, From 3c740d48936d379c98c0f2c453a5fb8c8de87af2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:21:21 +0000 Subject: [PATCH 09/33] Style extract_tts_spectrogram.py --- TTS/bin/extract_tts_spectrograms.py | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/TTS/bin/extract_tts_spectrograms.py b/TTS/bin/extract_tts_spectrograms.py index 112ef046..681fcc36 100755 --- a/TTS/bin/extract_tts_spectrograms.py +++ b/TTS/bin/extract_tts_spectrograms.py @@ -76,14 +76,14 @@ def set_filename(wav_path, out_path): def format_data(data): # setup input data - text_input = data['text'] - text_lengths = data['text_lengths'] - mel_input = data['mel'] - mel_lengths = data['mel_lengths'] - item_idx = data['item_idxs'] - d_vectors = data['d_vectors'] - speaker_ids = data['speaker_ids'] - attn_mask = data['attns'] + text_input = data["text"] + text_lengths = data["text_lengths"] + mel_input = data["mel"] + mel_lengths = data["mel_lengths"] + item_idx = data["item_idxs"] + d_vectors = data["d_vectors"] + speaker_ids = data["speaker_ids"] + attn_mask = data["attns"] avg_text_length = torch.mean(text_lengths.float()) avg_spec_length = torch.mean(mel_lengths.float()) @@ -132,7 +132,11 @@ def inference( elif d_vectors is not None: speaker_c = d_vectors outputs = model.inference_with_MAS( - text_input, text_lengths, mel_input, mel_lengths, aux_input={"d_vectors": speaker_c, "speaker_ids": speaker_ids} + text_input, + text_lengths, + mel_input, + mel_lengths, + aux_input={"d_vectors": speaker_c, "speaker_ids": speaker_ids}, ) model_output = outputs["model_outputs"] model_output = model_output.transpose(1, 2).detach().cpu().numpy() From 8b7e094bdec4ceebbef2fe2e000fc9a68e26fc57 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:24:33 +0000 Subject: [PATCH 10/33] Implement `forward_tts` - Generic API for feed-forward TTS models (FastPitch, SpeedySpeech) - Tests for `forward-tts` - Edit FastPitchConfig and SpeedySpeechConfig to use `forward_tts` --- TTS/tts/configs/fast_pitch_config.py | 43 +- TTS/tts/configs/speedy_speech_config.py | 119 +++- TTS/tts/models/forward_tts.py | 695 ++++++++++++++++++++++++ tests/tts_tests/test_forward_tts.py | 149 +++++ 4 files changed, 973 insertions(+), 33 deletions(-) create mode 100644 TTS/tts/models/forward_tts.py create mode 100644 tests/tts_tests/test_forward_tts.py diff --git a/TTS/tts/configs/fast_pitch_config.py b/TTS/tts/configs/fast_pitch_config.py index 873f298e..3840d1d6 100644 --- a/TTS/tts/configs/fast_pitch_config.py +++ b/TTS/tts/configs/fast_pitch_config.py @@ -2,12 +2,12 @@ from dataclasses import dataclass, field from typing import List from TTS.tts.configs.shared_configs import BaseTTSConfig -from TTS.tts.models.fast_pitch import FastPitchArgs +from TTS.tts.models.forward_tts import ForwardTTSArgs @dataclass class FastPitchConfig(BaseTTSConfig): - """Defines parameters for Speedy Speech (feed-forward encoder-decoder) based models. + """Configure `ForwardTTS` as FastPitch model. Example: @@ -36,22 +36,43 @@ class FastPitchConfig(BaseTTSConfig): d_vector_file (str): Path to the file including pre-computed speaker embeddings. Defaults to None. - noam_schedule (bool): - enable / disable the use of Noam LR scheduler. Defaults to False. + d_vector_dim (int): + Dimension of the external speaker embeddings. Defaults to 0. - warmup_steps (int): - Number of warm-up steps for the Noam scheduler. Defaults 4000. + optimizer (str): + Name of the model optimizer. Defaults to `Adam`. + + optimizer_params (dict): + Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`. + + lr_scheduler (str): + Name of the learning rate scheduler. Defaults to `Noam`. + + lr_scheduler_params (dict): + Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`. lr (float): Initial learning rate. Defaults to `1e-3`. + grad_clip (float): + Gradient norm clipping value. Defaults to `5.0`. + + spec_loss_type (str): + Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`. + + duration_loss_type (str): + Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`. + + use_ssim_loss (bool): + Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True. + wd (float): Weight decay coefficient. Defaults to `1e-7`. ssim_loss_alpha (float): Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0. - huber_loss_alpha (float): + dur_loss_alpha (float): Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0. spec_loss_alpha (float): @@ -73,9 +94,9 @@ class FastPitchConfig(BaseTTSConfig): Maximum input sequence length to be used at training. Larger values result in more VRAM usage. """ - model: str = "fast_pitch" + model: str = "forward_tts" # model specific params - model_args: FastPitchArgs = field(default_factory=FastPitchArgs) + model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs) # multi-speaker settings use_speaker_embedding: bool = False @@ -92,11 +113,13 @@ class FastPitchConfig(BaseTTSConfig): grad_clip: float = 5.0 # loss params + spec_loss_type: str = "mse" + duration_loss_type: str = "mse" + use_ssim_loss: bool = True ssim_loss_alpha: float = 1.0 dur_loss_alpha: float = 1.0 spec_loss_alpha: float = 1.0 pitch_loss_alpha: float = 1.0 - dur_loss_alpha: float = 1.0 aligner_loss_alpha: float = 1.0 binary_align_loss_alpha: float = 1.0 binary_align_loss_start_step: int = 20000 diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py index b2641ab5..bdfc2a6b 100644 --- a/TTS/tts/configs/speedy_speech_config.py +++ b/TTS/tts/configs/speedy_speech_config.py @@ -2,81 +2,154 @@ from dataclasses import dataclass, field from typing import List from TTS.tts.configs.shared_configs import BaseTTSConfig -from TTS.tts.models.speedy_speech import SpeedySpeechArgs +from TTS.tts.models.forward_tts import ForwardTTSArgs @dataclass class SpeedySpeechConfig(BaseTTSConfig): - """Defines parameters for Speedy Speech (feed-forward encoder-decoder) based models. + """Configure `ForwardTTS` as SpeedySpeech model. Example: >>> from TTS.tts.configs import SpeedySpeechConfig >>> config = SpeedySpeechConfig() - Args: + Args: model (str): - Model name used for selecting the right model at initialization. Defaults to `speedy_speech`. + Model name used for selecting the right model at initialization. Defaults to `fast_pitch`. + model_args (Coqpit): - Model class arguments. Check `SpeedySpeechArgs` for more details. Defaults to `SpeedySpeechArgs()`. + Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`. + data_dep_init_steps (int): Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses Activation Normalization that pre-computes normalization stats at the beginning and use the same values for the rest. Defaults to 10. + use_speaker_embedding (bool): enable / disable using speaker embeddings for multi-speaker models. If set True, the model is in the multi-speaker mode. Defaults to False. + use_d_vector_file (bool): enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False. + d_vector_file (str): Path to the file including pre-computed speaker embeddings. Defaults to None. - noam_schedule (bool): - enable / disable the use of Noam LR scheduler. Defaults to False. - warmup_steps (int): - Number of warm-up steps for the Noam scheduler. Defaults 4000. + + d_vector_dim (int): + Dimension of the external speaker embeddings. Defaults to 0. + + optimizer (str): + Name of the model optimizer. Defaults to `RAdam`. + + optimizer_params (dict): + Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`. + + lr_scheduler (str): + Name of the learning rate scheduler. Defaults to `Noam`. + + lr_scheduler_params (dict): + Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`. + lr (float): Initial learning rate. Defaults to `1e-3`. + + grad_clip (float): + Gradient norm clipping value. Defaults to `5.0`. + + spec_loss_type (str): + Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`. + + duration_loss_type (str): + Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`. + + use_ssim_loss (bool): + Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True. + wd (float): Weight decay coefficient. Defaults to `1e-7`. - ssim_alpha (float): - Weight for the SSIM loss. If set <= 0, disables the SSIM loss. Defaults to 1.0. - huber_alpha (float): - Weight for the duration predictor's loss. Defaults to 1.0. - l1_alpha (float): - Weight for the L1 spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0. + + ssim_loss_alpha (float): + Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0. + + dur_loss_alpha (float): + Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0. + + spec_loss_alpha (float): + Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0. + + binary_loss_alpha (float): + Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0. + + binary_align_loss_start_step (int): + Start binary alignment loss after this many steps. Defaults to 20000. + min_seq_len (int): Minimum input sequence length to be used at training. + max_seq_len (int): Maximum input sequence length to be used at training. Larger values result in more VRAM usage. """ - model: str = "speedy_speech" - # model specific params - model_args: SpeedySpeechArgs = field(default_factory=SpeedySpeechArgs) + model: str = "forward_tts" + + # set model args as SpeedySpeech + model_args: ForwardTTSArgs = ForwardTTSArgs( + use_pitch=False, + encoder_type="residual_conv_bn", + encoder_params={ + "kernel_size": 4, + "dilations": 4 * [1, 2, 4] + [1], + "num_conv_blocks": 2, + "num_res_blocks": 13, + }, + decoder_type="residual_conv_bn", + decoder_params={ + "kernel_size": 4, + "dilations": 4 * [1, 2, 4, 8] + [1], + "num_conv_blocks": 2, + "num_res_blocks": 17, + }, + out_channels=80, + hidden_channels=128, + num_speakers=0, + positional_encoding=True, + ) # multi-speaker settings use_speaker_embedding: bool = False use_d_vector_file: bool = False d_vector_file: str = False + d_vector_dim: int = 0 # optimizer parameters optimizer: str = "RAdam" optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6}) - lr_scheduler: str = None - lr_scheduler_params: dict = None + lr_scheduler: str = "NoamLR" + lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000}) lr: float = 1e-4 grad_clip: float = 5.0 # loss params - ssim_alpha: float = 1.0 - huber_alpha: float = 1.0 - l1_alpha: float = 1.0 + spec_loss_type: str = "l1" + duration_loss_type: str = "huber" + use_ssim_loss: bool = True + ssim_loss_alpha: float = 1.0 + dur_loss_alpha: float = 1.0 + spec_loss_alpha: float = 1.0 + aligner_loss_alpha: float = 1.0 + binary_align_loss_alpha: float = 1.0 + binary_align_loss_start_step: int = 20000 # overrides min_seq_len: int = 13 max_seq_len: int = 200 r: int = 1 # DO NOT CHANGE + # dataset configs + compute_f0: bool = False + f0_cache_path: str = None + # testing test_sentences: List[str] = field( default_factory=lambda: [ diff --git a/TTS/tts/models/forward_tts.py b/TTS/tts/models/forward_tts.py new file mode 100644 index 00000000..c4411027 --- /dev/null +++ b/TTS/tts/models/forward_tts.py @@ -0,0 +1,695 @@ +from dataclasses import dataclass, field +from typing import Dict, Tuple + +import torch +from coqpit import Coqpit +from torch import nn +from torch.cuda.amp.autocast_mode import autocast + +from TTS.tts.layers.feed_forward.decoder import Decoder +from TTS.tts.layers.feed_forward.encoder import Encoder +from TTS.tts.layers.generic.aligner import AlignmentNetwork +from TTS.tts.layers.generic.pos_encoding import PositionalEncoding +from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor +from TTS.tts.models.base_tts import BaseTTS +from TTS.tts.utils.helpers import average_over_durations, generate_path, maximum_path, sequence_mask +from TTS.tts.utils.visual import plot_alignment, plot_pitch, plot_spectrogram +from TTS.utils.audio import AudioProcessor + + +@dataclass +class ForwardTTSArgs(Coqpit): + """ForwardTTS Model arguments. + + Args: + + num_chars (int): + Number of characters in the vocabulary. Defaults to 100. + + out_channels (int): + Number of output channels. Defaults to 80. + + hidden_channels (int): + Number of base hidden channels of the model. Defaults to 512. + + num_speakers (int): + Number of speakers for the speaker embedding layer. Defaults to 0. + + use_aligner (bool): + Whether to use aligner network to learn the text to speech alignment or use pre-computed durations. + If set False, durations should be computed by `TTS/bin/compute_attention_masks.py` and path to the + pre-computed durations must be provided to `config.datasets[0].meta_file_attn_mask`. Defaults to True. + + use_pitch (bool): + Use pitch predictor to learn the pitch. Defaults to True. + + duration_predictor_hidden_channels (int): + Number of hidden channels in the duration predictor. Defaults to 256. + + duration_predictor_dropout_p (float): + Dropout rate for the duration predictor. Defaults to 0.1. + + duration_predictor_kernel_size (int): + Kernel size of conv layers in the duration predictor. Defaults to 3. + + pitch_predictor_hidden_channels (int): + Number of hidden channels in the pitch predictor. Defaults to 256. + + pitch_predictor_dropout_p (float): + Dropout rate for the pitch predictor. Defaults to 0.1. + + pitch_predictor_kernel_size (int): + Kernel size of conv layers in the pitch predictor. Defaults to 3. + + pitch_embedding_kernel_size (int): + Kernel size of the projection layer in the pitch predictor. Defaults to 3. + + positional_encoding (bool): + Whether to use positional encoding. Defaults to True. + + positional_encoding_use_scale (bool): + Whether to use a learnable scale coeff in the positional encoding. Defaults to True. + + length_scale (int): + Length scale that multiplies the predicted durations. Larger values result slower speech. Defaults to 1.0. + + encoder_type (str): + Type of the encoder module. One of the encoders available in :class:`TTS.tts.layers.feed_forward.encoder`. + Defaults to `fftransformer` as in the paper. + + encoder_params (dict): + Parameters of the encoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` + + decoder_type (str): + Type of the decoder module. One of the decoders available in :class:`TTS.tts.layers.feed_forward.decoder`. + Defaults to `fftransformer` as in the paper. + + decoder_params (str): + Parameters of the decoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` + + use_d_vetor (bool): + Whether to use precomputed d-vectors for multi-speaker training. Defaults to False. + + d_vector_dim (int): + Number of channels of the d-vectors. Defaults to 0. + + detach_duration_predictor (bool): + Detach the input to the duration predictor from the earlier computation graph so that the duraiton loss + does not pass to the earlier layers. Defaults to True. + + max_duration (int): + Maximum duration accepted by the model. Defaults to 75. + """ + + num_chars: int = None + out_channels: int = 80 + hidden_channels: int = 384 + num_speakers: int = 0 + use_aligner: bool = True + use_pitch: bool = True + pitch_predictor_hidden_channels: int = 256 + pitch_predictor_kernel_size: int = 3 + pitch_predictor_dropout_p: float = 0.1 + pitch_embedding_kernel_size: int = 3 + duration_predictor_hidden_channels: int = 256 + duration_predictor_kernel_size: int = 3 + duration_predictor_dropout_p: float = 0.1 + positional_encoding: bool = True + poisitonal_encoding_use_scale: bool = True + length_scale: int = 1 + encoder_type: str = "fftransformer" + encoder_params: dict = field( + default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} + ) + decoder_type: str = "fftransformer" + decoder_params: dict = field( + default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} + ) + use_d_vector: bool = False + d_vector_dim: int = 0 + detach_duration_predictor: bool = False + max_duration: int = 75 + + +class ForwardTTS(BaseTTS): + """General forward TTS model implementation that uses an encoder-decoder architecture with an optional alignment + network and a pitch predictor. + + If the alignment network is used, the model learns the text-to-speech alignment + from the data instead of using pre-computed durations. + + If the pitch predictor is used, the model trains a pitch predictor that predicts average pitch value for each + input character as in the FastPitch model. + + `ForwardTTS` can be configured to one of these architectures, + + - FastPitch + - SpeedySpeech + - FastSpeech + - TODO: FastSpeech2 (requires average speech energy predictor) + + Args: + config (Coqpit): Model coqpit class. + + Examples: + >>> from TTS.tts.models.fast_pitch import ForwardTTS, ForwardTTSArgs + >>> config = ForwardTTSArgs() + >>> model = ForwardTTS(config) + """ + + # pylint: disable=dangerous-default-value + def __init__(self, config: Coqpit): + + super().__init__() + + # don't use isintance not to import recursively + if "Config" in config.__class__.__name__: + if "characters" in config: + # loading from FasrPitchConfig + _, self.config, num_chars = self.get_characters(config) + config.model_args.num_chars = num_chars + self.args = self.config.model_args + else: + # loading from ForwardTTSArgs + self.config = config + self.args = config.model_args + elif isinstance(config, ForwardTTSArgs): + self.args = config + self.config = config + else: + raise ValueError("config must be either a *Config or ForwardTTSArgs") + + self.max_duration = self.args.max_duration + self.use_aligner = self.args.use_aligner + self.use_pitch = self.args.use_pitch + self.use_binary_alignment_loss = False + + self.length_scale = ( + float(self.args.length_scale) if isinstance(self.args.length_scale, int) else self.args.length_scale + ) + + self.emb = nn.Embedding(self.args.num_chars, self.args.hidden_channels) + + self.encoder = Encoder( + self.args.hidden_channels, + self.args.hidden_channels, + self.args.encoder_type, + self.args.encoder_params, + self.args.d_vector_dim, + ) + + if self.args.positional_encoding: + self.pos_encoder = PositionalEncoding(self.args.hidden_channels) + + self.decoder = Decoder( + self.args.out_channels, + self.args.hidden_channels, + self.args.decoder_type, + self.args.decoder_params, + ) + + self.duration_predictor = DurationPredictor( + self.args.hidden_channels + self.args.d_vector_dim, + self.args.duration_predictor_hidden_channels, + self.args.duration_predictor_kernel_size, + self.args.duration_predictor_dropout_p, + ) + + if self.args.use_pitch: + self.pitch_predictor = DurationPredictor( + self.args.hidden_channels + self.args.d_vector_dim, + self.args.pitch_predictor_hidden_channels, + self.args.pitch_predictor_kernel_size, + self.args.pitch_predictor_dropout_p, + ) + self.pitch_emb = nn.Conv1d( + 1, + self.args.hidden_channels, + kernel_size=self.args.pitch_embedding_kernel_size, + padding=int((self.args.pitch_embedding_kernel_size - 1) / 2), + ) + + if self.args.num_speakers > 1 and not self.args.use_d_vector: + # speaker embedding layer + self.emb_g = nn.Embedding(self.args.num_speakers, self.args.d_vector_dim) + nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) + + if self.args.d_vector_dim > 0 and self.args.d_vector_dim != self.args.hidden_channels: + self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1) + + if self.args.use_aligner: + self.aligner = AlignmentNetwork( + in_query_channels=self.args.out_channels, in_key_channels=self.args.hidden_channels + ) + + @staticmethod + def generate_attn(dr, x_mask, y_mask=None): + """Generate an attention mask from the durations. + + Shapes + - dr: :math:`(B, T_{en})` + - x_mask: :math:`(B, T_{en})` + - y_mask: :math:`(B, T_{de})` + """ + # compute decode mask from the durations + if y_mask is None: + y_lengths = dr.sum(1).long() + y_lengths[y_lengths < 1] = 1 + y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype) + attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) + attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype) + return attn + + def expand_encoder_outputs(self, en, dr, x_mask, y_mask): + """Generate attention alignment map from durations and + expand encoder outputs + + Shapes + - en: :math:`(B, D_{en}, T_{en})` + - dr: :math:`(B, T_{en})` + - x_mask: :math:`(B, T_{en})` + - y_mask: :math:`(B, T_{de})` + + Examples: + - encoder output: :math:`[a,b,c,d]` + - durations: :math:`[1, 3, 2, 1]` + + - expanded: :math:`[a, b, b, b, c, c, d]` + - attention map: :math:`[[0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]]` + """ + attn = self.generate_attn(dr, x_mask, y_mask) + o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2).to(en.dtype), en.transpose(1, 2)).transpose(1, 2) + return o_en_ex, attn + + def format_durations(self, o_dr_log, x_mask): + """Format predicted durations. + 1. Convert to linear scale from log scale + 2. Apply the length scale for speed adjustment + 3. Apply masking. + 4. Cast 0 durations to 1. + 5. Round the duration values. + + Args: + o_dr_log: Log scale durations. + x_mask: Input text mask. + + Shapes: + - o_dr_log: :math:`(B, T_{de})` + - x_mask: :math:`(B, T_{en})` + """ + o_dr = (torch.exp(o_dr_log) - 1) * x_mask * self.length_scale + o_dr[o_dr < 1] = 1.0 + o_dr = torch.round(o_dr) + return o_dr + + @staticmethod + def _concat_speaker_embedding(o_en, g): + g_exp = g.expand(-1, -1, o_en.size(-1)) # [B, C, T_en] + o_en = torch.cat([o_en, g_exp], 1) + return o_en + + def _sum_speaker_embedding(self, x, g): + # project g to decoder dim. + if hasattr(self, "proj_g"): + g = self.proj_g(g) + return x + g + + def _forward_encoder( + self, x: torch.LongTensor, x_mask: torch.FloatTensor, g: torch.FloatTensor = None + ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Encoding forward pass. + + 1. Embed speaker IDs if multi-speaker mode. + 2. Embed character sequences. + 3. Run the encoder network. + 4. Concat speaker embedding to the encoder output for the duration predictor. + + Args: + x (torch.LongTensor): Input sequence IDs. + x_mask (torch.FloatTensor): Input squence mask. + g (torch.FloatTensor, optional): Conditioning vectors. In general speaker embeddings. Defaults to None. + + Returns: + Tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor]: + encoder output, encoder output for the duration predictor, input sequence mask, speaker embeddings, + character embeddings + + Shapes: + - x: :math:`(B, T_{en})` + - x_mask: :math:`(B, 1, T_{en})` + - g: :math:`(B, C)` + """ + if hasattr(self, "emb_g"): + g = nn.functional.normalize(self.emb_g(g)) # [B, C, 1] + if g is not None: + g = g.unsqueeze(-1) + # [B, T, C] + x_emb = self.emb(x) + # encoder pass + o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask) + # speaker conditioning for duration predictor + if g is not None: + o_en_dp = self._concat_speaker_embedding(o_en, g) + else: + o_en_dp = o_en + return o_en, o_en_dp, x_mask, g, x_emb + + def _forward_decoder( + self, + o_en: torch.FloatTensor, + dr: torch.IntTensor, + x_mask: torch.FloatTensor, + y_lengths: torch.IntTensor, + g: torch.FloatTensor, + ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + """Decoding forward pass. + + 1. Compute the decoder output mask + 2. Expand encoder output with the durations. + 3. Apply position encoding. + 4. Add speaker embeddings if multi-speaker mode. + 5. Run the decoder. + + Args: + o_en (torch.FloatTensor): Encoder output. + dr (torch.IntTensor): Ground truth durations or alignment network durations. + x_mask (torch.IntTensor): Input sequence mask. + y_lengths (torch.IntTensor): Output sequence lengths. + g (torch.FloatTensor): Conditioning vectors. In general speaker embeddings. + + Returns: + Tuple[torch.FloatTensor, torch.FloatTensor]: Decoder output, attention map from durations. + """ + y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en.dtype) + # expand o_en with durations + o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask) + # positional encoding + if hasattr(self, "pos_encoder"): + o_en_ex = self.pos_encoder(o_en_ex, y_mask) + # speaker embedding + if g is not None: + o_en_ex = self._sum_speaker_embedding(o_en_ex, g) + # decoder pass + o_de = self.decoder(o_en_ex, y_mask, g=g) + return o_de.transpose(1, 2), attn.transpose(1, 2) + + def _forward_pitch_predictor( + self, + o_en: torch.FloatTensor, + x_mask: torch.IntTensor, + pitch: torch.FloatTensor = None, + dr: torch.IntTensor = None, + ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: + """Pitch predictor forward pass. + + 1. Predict pitch from encoder outputs. + 2. In training - Compute average pitch values for each input character from the ground truth pitch values. + 3. Embed average pitch values. + + Args: + o_en (torch.FloatTensor): Encoder output. + x_mask (torch.IntTensor): Input sequence mask. + pitch (torch.FloatTensor, optional): Ground truth pitch values. Defaults to None. + dr (torch.IntTensor, optional): Ground truth durations. Defaults to None. + + Returns: + Tuple[torch.FloatTensor, torch.FloatTensor]: Pitch embedding, pitch prediction. + + Shapes: + - o_en: :math:`(B, C, T_{en})` + - x_mask: :math:`(B, 1, T_{en})` + - pitch: :math:`(B, 1, T_{de})` + - dr: :math:`(B, T_{en})` + """ + o_pitch = self.pitch_predictor(o_en, x_mask) + if pitch is not None: + avg_pitch = average_over_durations(pitch, dr) + o_pitch_emb = self.pitch_emb(avg_pitch) + return o_pitch_emb, o_pitch, avg_pitch + o_pitch_emb = self.pitch_emb(o_pitch) + return o_pitch_emb, o_pitch + + def _forward_aligner( + self, x: torch.FloatTensor, y: torch.FloatTensor, x_mask: torch.IntTensor, y_mask: torch.IntTensor + ) -> Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + """Aligner forward pass. + + 1. Compute a mask to apply to the attention map. + 2. Run the alignment network. + 3. Apply MAS to compute the hard alignment map. + 4. Compute the durations from the hard alignment map. + + Args: + x (torch.FloatTensor): Input sequence. + y (torch.FloatTensor): Output sequence. + x_mask (torch.IntTensor): Input sequence mask. + y_mask (torch.IntTensor): Output sequence mask. + + Returns: + Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: + Durations from the hard alignment map, soft alignment potentials, log scale alignment potentials, + hard alignment map. + + Shapes: + - x: :math:`[B, T_en, C_en]` + - y: :math:`[B, T_de, C_de]` + - x_mask: :math:`[B, 1, T_en]` + - y_mask: :math:`[B, 1, T_de]` + + - o_alignment_dur: :math:`[B, T_en]` + - alignment_soft: :math:`[B, T_en, T_de]` + - alignment_logprob: :math:`[B, 1, T_de, T_en]` + - alignment_mas: :math:`[B, T_en, T_de]` + """ + attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) + alignment_soft, alignment_logprob = self.aligner(y.transpose(1, 2), x.transpose(1, 2), x_mask, None) + alignment_mas = maximum_path( + alignment_soft.squeeze(1).transpose(1, 2).contiguous(), attn_mask.squeeze(1).contiguous() + ) + o_alignment_dur = torch.sum(alignment_mas, -1).int() + alignment_soft = alignment_soft.squeeze(1).transpose(1, 2) + return o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas + + def forward( + self, + x: torch.LongTensor, + x_lengths: torch.LongTensor, + y_lengths: torch.LongTensor, + y: torch.FloatTensor = None, + dr: torch.IntTensor = None, + pitch: torch.FloatTensor = None, + aux_input: Dict = {"d_vectors": None, "speaker_ids": None}, # pylint: disable=unused-argument + ) -> Dict: + """Model's forward pass. + + Args: + x (torch.LongTensor): Input character sequences. + x_lengths (torch.LongTensor): Input sequence lengths. + y_lengths (torch.LongTensor): Output sequnce lengths. Defaults to None. + y (torch.FloatTensor): Spectrogram frames. Only used when the alignment network is on. Defaults to None. + dr (torch.IntTensor): Character durations over the spectrogram frames. Only used when the alignment network is off. Defaults to None. + pitch (torch.FloatTensor): Pitch values for each spectrogram frame. Only used when the pitch predictor is on. Defaults to None. + aux_input (Dict): Auxiliary model inputs for multi-speaker training. Defaults to `{"d_vectors": 0, "speaker_ids": None}`. + + Shapes: + - x: :math:`[B, T_max]` + - x_lengths: :math:`[B]` + - y_lengths: :math:`[B]` + - y: :math:`[B, T_max2]` + - dr: :math:`[B, T_max]` + - g: :math:`[B, C]` + - pitch: :math:`[B, 1, T]` + """ + g = aux_input["d_vectors"] if "d_vectors" in aux_input else None + # compute sequence masks + y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).float() + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).float() + # encoder pass + o_en, o_en_dp, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g) + # duration predictor pass + if self.args.detach_duration_predictor: + o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask) + else: + o_dr_log = self.duration_predictor(o_en_dp, x_mask) + o_dr = torch.clamp(torch.exp(o_dr_log) - 1, 0, self.max_duration) + # generate attn mask from predicted durations + o_attn = self.generate_attn(o_dr.squeeze(1), x_mask) + # aligner + o_alignment_dur = None + alignment_soft = None + alignment_logprob = None + alignment_mas = None + if self.use_aligner: + o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas = self._forward_aligner( + x_emb, y, x_mask, y_mask + ) + alignment_soft = alignment_soft.transpose(1, 2) + alignment_mas = alignment_mas.transpose(1, 2) + dr = o_alignment_dur + # pitch predictor pass + o_pitch = None + avg_pitch = None + if self.args.use_pitch: + o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en_dp, x_mask, pitch, dr) + o_en = o_en + o_pitch_emb + # decoder pass + o_de, attn = self._forward_decoder(o_en, dr, x_mask, y_lengths, g=g) + outputs = { + "model_outputs": o_de, # [B, T, C] + "durations_log": o_dr_log.squeeze(1), # [B, T] + "durations": o_dr.squeeze(1), # [B, T] + "attn_durations": o_attn, # for visualization [B, T_en, T_de'] + "pitch_avg": o_pitch, + "pitch_avg_gt": avg_pitch, + "alignments": attn, # [B, T_de, T_en] + "alignment_soft": alignment_soft, + "alignment_mas": alignment_mas, + "o_alignment_dur": o_alignment_dur, + "alignment_logprob": alignment_logprob, + "x_mask": x_mask, + "y_mask": y_mask, + } + return outputs + + @torch.no_grad() + def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument + """Model's inference pass. + + Args: + x (torch.LongTensor): Input character sequence. + aux_input (Dict): Auxiliary model inputs. Defaults to `{"d_vectors": None, "speaker_ids": None}`. + + Shapes: + - x: [B, T_max] + - x_lengths: [B] + - g: [B, C] + """ + g = aux_input["d_vectors"] if "d_vectors" in aux_input else None + x_lengths = torch.tensor(x.shape[1:2]).to(x.device) + x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype).float() + # encoder pass + o_en, o_en_dp, x_mask, g, _ = self._forward_encoder(x, x_mask, g) + # duration predictor pass + o_dr_log = self.duration_predictor(o_en_dp, x_mask) + o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1) + y_lengths = o_dr.sum(1) + # pitch predictor pass + o_pitch = None + if self.args.use_pitch: + o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en_dp, x_mask) + o_en = o_en + o_pitch_emb + # decoder pass + o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=g) + outputs = { + "model_outputs": o_de, + "alignments": attn, + "pitch": o_pitch, + "durations_log": o_dr_log, + } + return outputs + + def train_step(self, batch: dict, criterion: nn.Module): + text_input = batch["text_input"] + text_lengths = batch["text_lengths"] + mel_input = batch["mel_input"] + mel_lengths = batch["mel_lengths"] + pitch = batch["pitch"] if self.args.use_pitch else None + d_vectors = batch["d_vectors"] + speaker_ids = batch["speaker_ids"] + durations = batch["durations"] + aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids} + + # forward pass + outputs = self.forward( + text_input, text_lengths, mel_lengths, y=mel_input, dr=durations, pitch=pitch, aux_input=aux_input + ) + # use aligner's output as the duration target + if self.use_aligner: + durations = outputs["o_alignment_dur"] + # use float32 in AMP + with autocast(enabled=False): + # compute loss + loss_dict = criterion( + decoder_output=outputs["model_outputs"], + decoder_target=mel_input, + decoder_output_lens=mel_lengths, + dur_output=outputs["durations_log"], + dur_target=durations, + pitch_output=outputs["pitch_avg"] if self.use_pitch else None, + pitch_target=outputs["pitch_avg_gt"] if self.use_pitch else None, + input_lens=text_lengths, + alignment_logprob=outputs["alignment_logprob"] if self.use_aligner else None, + alignment_soft=outputs["alignment_soft"] if self.use_binary_alignment_loss else None, + alignment_hard=outputs["alignment_mas"] if self.use_binary_alignment_loss else None, + ) + # compute duration error + durations_pred = outputs["durations"] + duration_error = torch.abs(durations - durations_pred).sum() / text_lengths.sum() + loss_dict["duration_error"] = duration_error + + return outputs, loss_dict + + def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use + model_outputs = outputs["model_outputs"] + alignments = outputs["alignments"] + mel_input = batch["mel_input"] + + pred_spec = model_outputs[0].data.cpu().numpy() + gt_spec = mel_input[0].data.cpu().numpy() + align_img = alignments[0].data.cpu().numpy() + + figures = { + "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), + "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), + "alignment": plot_alignment(align_img, output_fig=False), + } + + # plot pitch figures + if self.args.use_pitch: + pitch = batch["pitch"] + pitch_avg_expanded, _ = self.expand_encoder_outputs( + outputs["pitch_avg"], outputs["durations"], outputs["x_mask"], outputs["y_mask"] + ) + pitch = pitch[0, 0].data.cpu().numpy() + # TODO: denormalize before plotting + pitch = abs(pitch) + pitch_avg_expanded = abs(pitch_avg_expanded[0, 0]).data.cpu().numpy() + pitch_figures = { + "pitch_ground_truth": plot_pitch(pitch, gt_spec, ap, output_fig=False), + "pitch_avg_predicted": plot_pitch(pitch_avg_expanded, pred_spec, ap, output_fig=False), + } + figures.update(pitch_figures) + + # plot the attention mask computed from the predicted durations + if "attn_durations" in outputs: + alignments_hat = outputs["attn_durations"][0].data.cpu().numpy() + figures["alignment_hat"] = plot_alignment(alignments_hat.T, output_fig=False) + + # Sample audio + train_audio = ap.inv_melspectrogram(pred_spec.T) + return figures, {"audio": train_audio} + + def eval_step(self, batch: dict, criterion: nn.Module): + return self.train_step(batch, criterion) + + def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): + return self.train_log(ap, batch, outputs) + + def load_checkpoint( + self, config, checkpoint_path, eval=False + ): # pylint: disable=unused-argument, redefined-builtin + state = torch.load(checkpoint_path, map_location=torch.device("cpu")) + self.load_state_dict(state["model"]) + if eval: + self.eval() + assert not self.training + + def get_criterion(self): + from TTS.tts.layers.losses import ForwardTTSLoss # pylint: disable=import-outside-toplevel + + return ForwardTTSLoss(self.config) + + def on_train_step_start(self, trainer): + """Enable binary alignment loss when needed""" + if trainer.total_steps_done > self.config.binary_align_loss_start_step: + self.use_binary_alignment_loss = True diff --git a/tests/tts_tests/test_forward_tts.py b/tests/tts_tests/test_forward_tts.py new file mode 100644 index 00000000..9bb60f48 --- /dev/null +++ b/tests/tts_tests/test_forward_tts.py @@ -0,0 +1,149 @@ +import unittest + +import torch as T + +from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs +from TTS.tts.utils.helpers import sequence_mask + +# pylint: disable=unused-variable + + +def expand_encoder_outputs_test(): + model = ForwardTTS(ForwardTTSArgs(num_chars=10)) + + inputs = T.rand(2, 5, 57) + durations = T.randint(1, 4, (2, 57)) + + x_mask = T.ones(2, 1, 57) + y_mask = T.ones(2, 1, durations.sum(1).max()) + + expanded, _ = model.expand_encoder_outputs(inputs, durations, x_mask, y_mask) + + for b in range(durations.shape[0]): + index = 0 + for idx, dur in enumerate(durations[b]): + diff = ( + expanded[b, :, index : index + dur.item()] + - inputs[b, :, idx].repeat(dur.item()).view(expanded[b, :, index : index + dur.item()].shape) + ).sum() + assert abs(diff) < 1e-6, diff + index += dur + + +def model_input_output_test(): + """Assert the output shapes of the model in different modes""" + + # VANILLA MODEL + model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=False)) + + x = T.randint(0, 10, (2, 21)) + x_lengths = T.randint(10, 22, (2,)) + x_lengths[-1] = 21 + x_mask = sequence_mask(x_lengths).unsqueeze(1).long() + durations = T.randint(1, 4, (2, 21)) + durations = durations * x_mask.squeeze(1) + y_lengths = durations.sum(1) + y_mask = sequence_mask(y_lengths).unsqueeze(1).long() + + outputs = model.forward(x, x_lengths, y_lengths, dr=durations) + + assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) + assert outputs["durations_log"].shape == (2, 21) + assert outputs["durations"].shape == (2, 21) + assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) + assert (outputs["x_mask"] - x_mask).sum() == 0.0 + assert (outputs["y_mask"] - y_mask).sum() == 0.0 + + assert outputs["alignment_soft"] == None + assert outputs["alignment_mas"] == None + assert outputs["alignment_logprob"] == None + assert outputs["o_alignment_dur"] == None + assert outputs["pitch_avg"] == None + assert outputs["pitch_avg_gt"] == None + + # USE PITCH + model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False)) + + x = T.randint(0, 10, (2, 21)) + x_lengths = T.randint(10, 22, (2,)) + x_lengths[-1] = 21 + x_mask = sequence_mask(x_lengths).unsqueeze(1).long() + durations = T.randint(1, 4, (2, 21)) + durations = durations * x_mask.squeeze(1) + y_lengths = durations.sum(1) + y_mask = sequence_mask(y_lengths).unsqueeze(1).long() + pitch = T.rand(2, 1, y_lengths.max()) + + outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch) + + assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) + assert outputs["durations_log"].shape == (2, 21) + assert outputs["durations"].shape == (2, 21) + assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) + assert (outputs["x_mask"] - x_mask).sum() == 0.0 + assert (outputs["y_mask"] - y_mask).sum() == 0.0 + assert outputs["pitch_avg"].shape == (2, 1, 21) + assert outputs["pitch_avg_gt"].shape == (2, 1, 21) + + assert outputs["alignment_soft"] == None + assert outputs["alignment_mas"] == None + assert outputs["alignment_logprob"] == None + assert outputs["o_alignment_dur"] == None + + # USE ALIGNER NETWORK + model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True)) + + x = T.randint(0, 10, (2, 21)) + x_lengths = T.randint(10, 22, (2,)) + x_lengths[-1] = 21 + x_mask = sequence_mask(x_lengths).unsqueeze(1).long() + durations = T.randint(1, 4, (2, 21)) + durations = durations * x_mask.squeeze(1) + y_lengths = durations.sum(1) + y_mask = sequence_mask(y_lengths).unsqueeze(1).long() + y = T.rand(2, y_lengths.max(), 80) + + outputs = model.forward(x, x_lengths, y_lengths, dr=durations, y=y) + + assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) + assert outputs["durations_log"].shape == (2, 21) + assert outputs["durations"].shape == (2, 21) + assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) + assert (outputs["x_mask"] - x_mask).sum() == 0.0 + assert (outputs["y_mask"] - y_mask).sum() == 0.0 + assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21) + assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21) + assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21) + assert outputs["o_alignment_dur"].shape == (2, 21) + + assert outputs["pitch_avg"] == None + assert outputs["pitch_avg_gt"] == None + + # USE ALIGNER NETWORK AND PITCH + model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True)) + + x = T.randint(0, 10, (2, 21)) + x_lengths = T.randint(10, 22, (2,)) + x_lengths[-1] = 21 + x_mask = sequence_mask(x_lengths).unsqueeze(1).long() + durations = T.randint(1, 4, (2, 21)) + durations = durations * x_mask.squeeze(1) + y_lengths = durations.sum(1) + y_mask = sequence_mask(y_lengths).unsqueeze(1).long() + y = T.rand(2, y_lengths.max(), 80) + pitch = T.rand(2, 1, y_lengths.max()) + + outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch, y=y) + + assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80) + assert outputs["durations_log"].shape == (2, 21) + assert outputs["durations"].shape == (2, 21) + assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21) + assert (outputs["x_mask"] - x_mask).sum() == 0.0 + assert (outputs["y_mask"] - y_mask).sum() == 0.0 + assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21) + assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21) + assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21) + assert outputs["o_alignment_dur"].shape == (2, 21) + assert outputs["pitch_avg"].shape == (2, 1, 21) + assert outputs["pitch_avg_gt"].shape == (2, 1, 21) From ed4b1d8514200e5c79634d7c7152f1d569806d4f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:25:21 +0000 Subject: [PATCH 11/33] Test `TTS.tts.utils.helpers` --- TTS/tts/utils/helpers.py | 12 +++---- tests/tts_tests/test_helpers.py | 60 +++++++++++++++++++++++++++++++++ 2 files changed, 65 insertions(+), 7 deletions(-) create mode 100644 tests/tts_tests/test_helpers.py diff --git a/TTS/tts/utils/helpers.py b/TTS/tts/utils/helpers.py index 5ec6601a..76abf2bc 100644 --- a/TTS/tts/utils/helpers.py +++ b/TTS/tts/utils/helpers.py @@ -1,6 +1,3 @@ -import torch -import numpy as np - import numpy as np import torch from torch.nn import functional as F @@ -14,9 +11,9 @@ except ModuleNotFoundError: class StandardScaler: - """StandardScaler for mean-std normalization with the given mean and std values. - """ - def __init__(self, mean:np.ndarray=None, std:np.ndarray=None) -> None: + """StandardScaler for mean-std normalization with the given mean and std values.""" + + def __init__(self, mean: np.ndarray = None, std: np.ndarray = None) -> None: self.mean_ = mean self.std_ = std @@ -97,6 +94,7 @@ def rand_segments(x: torch.tensor, x_lengths: torch.tensor = None, segment_size= ret = segment(x, segment_indices, segment_size) return ret, segment_indices + def average_over_durations(values, durs): """Average values over durations. @@ -212,4 +210,4 @@ def maximum_path_numpy(value, mask, max_neg_val=None): index = index + direction[index_range, index, j] - 1 path = path * mask.astype(np.float32) path = torch.from_numpy(path).to(device=device, dtype=dtype) - return path \ No newline at end of file + return path diff --git a/tests/tts_tests/test_helpers.py b/tests/tts_tests/test_helpers.py new file mode 100644 index 00000000..0aac5473 --- /dev/null +++ b/tests/tts_tests/test_helpers.py @@ -0,0 +1,60 @@ +import torch as T + +from TTS.tts.utils.helpers import * + + +def average_over_durations_test(): # pylint: disable=no-self-use + pitch = T.rand(1, 1, 128) + + durations = T.randint(1, 5, (1, 21)) + coeff = 128.0 / durations.sum() + durations = T.floor(durations * coeff) + diff = 128.0 - durations.sum() + durations[0, -1] += diff + durations = durations.long() + + pitch_avg = average_over_durations(pitch, durations) + + index = 0 + for idx, dur in enumerate(durations[0]): + assert abs(pitch_avg[0, 0, idx] - pitch[0, 0, index : index + dur.item()].mean()) < 1e-5 + index += dur + + +def seqeunce_mask_test(): + lengths = T.randint(10, 15, (8,)) + mask = sequence_mask(lengths) + for i in range(8): + l = lengths[i].item() + assert mask[i, :l].sum() == l + assert mask[i, l:].sum() == 0 + + +def segment_test(): + x = T.range(0, 11) + x = x.repeat(8, 1).unsqueeze(1) + segment_ids = T.randint(0, 7, (8,)) + + segments = segment(x, segment_ids, segment_size=4) + for idx, start_indx in enumerate(segment_ids): + assert x[idx, :, start_indx : start_indx + 4].sum() == segments[idx, :, :].sum() + + +def generate_path_test(): + durations = T.randint(1, 4, (10, 21)) + x_length = T.randint(18, 22, (10,)) + x_mask = sequence_mask(x_length).unsqueeze(1).long() + durations = durations * x_mask.squeeze(1) + y_length = durations.sum(1) + y_mask = sequence_mask(y_length).unsqueeze(1).long() + attn_mask = (torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)).squeeze(1).long() + print(attn_mask.shape) + path = generate_path(durations, attn_mask) + assert path.shape == (10, 21, durations.sum(1).max().item()) + for b in range(durations.shape[0]): + current_idx = 0 + for t in range(durations.shape[1]): + assert all(path[b, t, current_idx : current_idx + durations[b, t].item()] == 1.0) + assert all(path[b, t, :current_idx] == 0.0) + assert all(path[b, t, current_idx + durations[b, t].item() :] == 0.0) + current_idx += durations[b, t].item() From 742f9c54da050516fb01c4cb28677f950e349bc8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:26:05 +0000 Subject: [PATCH 12/33] Warn user if nan in GL --- TTS/utils/audio.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/TTS/utils/audio.py b/TTS/utils/audio.py index a8952afc..45c061d2 100644 --- a/TTS/utils/audio.py +++ b/TTS/utils/audio.py @@ -608,6 +608,9 @@ class AudioProcessor(object): angles = np.exp(2j * np.pi * np.random.rand(*S.shape)) S_complex = np.abs(S).astype(np.complex) y = self._istft(S_complex * angles) + if not np.isfinite(y).all(): + print(" [!] Waveform is not finite everywhere. Skipping the GL.") + return np.array([0]) for _ in range(self.griffin_lim_iters): angles = np.exp(1j * np.angle(self._stft(y))) y = self._istft(S_complex * angles) From 3c160131996224569ddc68678fcc98f9f1d90f14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:26:34 +0000 Subject: [PATCH 13/33] Fix Vits imports --- TTS/tts/models/vits.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/TTS/tts/models/vits.py b/TTS/tts/models/vits.py index 52f34e6e..87695774 100644 --- a/TTS/tts/models/vits.py +++ b/TTS/tts/models/vits.py @@ -9,12 +9,11 @@ from torch import nn from torch.cuda.amp.autocast_mode import autocast from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor -from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.layers.vits.discriminator import VitsDiscriminator from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks, TextEncoder from TTS.tts.layers.vits.stochastic_duration_predictor import StochasticDurationPredictor from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.helpers import sequence_mask +from TTS.tts.utils.helpers import generate_path, maximum_path, rand_segments, segment, sequence_mask from TTS.tts.utils.speakers import get_speaker_manager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.visual import plot_alignment @@ -22,7 +21,6 @@ from TTS.utils.audio import AudioProcessor from TTS.utils.trainer_utils import get_optimizer, get_scheduler from TTS.vocoder.models.hifigan_generator import HifiganGenerator from TTS.vocoder.utils.generic_utils import plot_results -from TTS.tts.utils.helpers import rand_segments, segment @dataclass From 0541a25e900007a446cf1a7688d855f8eb9153b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:27:48 +0000 Subject: [PATCH 14/33] Remove `fastpitch.py` and `speedy_speech.py` --- TTS/tts/models/fast_pitch.py | 673 -------------------------------- TTS/tts/models/speedy_speech.py | 320 --------------- 2 files changed, 993 deletions(-) delete mode 100644 TTS/tts/models/fast_pitch.py delete mode 100644 TTS/tts/models/speedy_speech.py diff --git a/TTS/tts/models/fast_pitch.py b/TTS/tts/models/fast_pitch.py deleted file mode 100644 index 984d7d1a..00000000 --- a/TTS/tts/models/fast_pitch.py +++ /dev/null @@ -1,673 +0,0 @@ -from dataclasses import dataclass, field -from typing import Dict, Tuple - -import torch -from coqpit import Coqpit -from torch import nn -from torch.cuda.amp.autocast_mode import autocast - -from TTS.tts.layers.feed_forward.decoder import Decoder -from TTS.tts.layers.feed_forward.encoder import Encoder -from TTS.tts.layers.generic.aligner import AlignmentNetwork -from TTS.tts.layers.generic.pos_encoding import PositionalEncoding -from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor -from TTS.tts.utils.helpers import generate_path, maximum_path -from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.helpers import sequence_mask -from TTS.tts.utils.visual import plot_alignment, plot_pitch, plot_spectrogram -from TTS.utils.audio import AudioProcessor -from TTS.tts.utils.helpers import average_over_durations - - -@dataclass -class FastPitchArgs(Coqpit): - """Fast Pitch Model arguments. - - Args: - - num_chars (int): - Number of characters in the vocabulary. Defaults to 100. - - out_channels (int): - Number of output channels. Defaults to 80. - - hidden_channels (int): - Number of base hidden channels of the model. Defaults to 512. - - num_speakers (int): - Number of speakers for the speaker embedding layer. Defaults to 0. - - duration_predictor_hidden_channels (int): - Number of hidden channels in the duration predictor. Defaults to 256. - - duration_predictor_dropout_p (float): - Dropout rate for the duration predictor. Defaults to 0.1. - - duration_predictor_kernel_size (int): - Kernel size of conv layers in the duration predictor. Defaults to 3. - - pitch_predictor_hidden_channels (int): - Number of hidden channels in the pitch predictor. Defaults to 256. - - pitch_predictor_dropout_p (float): - Dropout rate for the pitch predictor. Defaults to 0.1. - - pitch_predictor_kernel_size (int): - Kernel size of conv layers in the pitch predictor. Defaults to 3. - - pitch_embedding_kernel_size (int): - Kernel size of the projection layer in the pitch predictor. Defaults to 3. - - positional_encoding (bool): - Whether to use positional encoding. Defaults to True. - - positional_encoding_use_scale (bool): - Whether to use a learnable scale coeff in the positional encoding. Defaults to True. - - length_scale (int): - Length scale that multiplies the predicted durations. Larger values result slower speech. Defaults to 1.0. - - encoder_type (str): - Type of the encoder module. One of the encoders available in :class:`TTS.tts.layers.feed_forward.encoder`. - Defaults to `fftransformer` as in the paper. - - encoder_params (dict): - Parameters of the encoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` - - decoder_type (str): - Type of the decoder module. One of the decoders available in :class:`TTS.tts.layers.feed_forward.decoder`. - Defaults to `fftransformer` as in the paper. - - decoder_params (str): - Parameters of the decoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}``` - - use_d_vetor (bool): - Whether to use precomputed d-vectors for multi-speaker training. Defaults to False. - - d_vector_dim (int): - Number of channels of the d-vectors. Defaults to 0. - - detach_duration_predictor (bool): - Detach the input to the duration predictor from the earlier computation graph so that the duraiton loss - does not pass to the earlier layers. Defaults to True. - - max_duration (int): - Maximum duration accepted by the model. Defaults to 75. - - use_aligner (bool): - Use aligner network to learn the text to speech alignment. Defaults to True. - """ - - num_chars: int = None - out_channels: int = 80 - hidden_channels: int = 384 - num_speakers: int = 0 - duration_predictor_hidden_channels: int = 256 - duration_predictor_kernel_size: int = 3 - duration_predictor_dropout_p: float = 0.1 - pitch_predictor_hidden_channels: int = 256 - pitch_predictor_kernel_size: int = 3 - pitch_predictor_dropout_p: float = 0.1 - pitch_embedding_kernel_size: int = 3 - positional_encoding: bool = True - poisitonal_encoding_use_scale: bool = True - length_scale: int = 1 - encoder_type: str = "fftransformer" - encoder_params: dict = field( - default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} - ) - decoder_type: str = "fftransformer" - decoder_params: dict = field( - default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1} - ) - use_d_vector: bool = False - d_vector_dim: int = 0 - detach_duration_predictor: bool = False - max_duration: int = 75 - use_aligner: bool = True - - -class FastPitch(BaseTTS): - """FastPitch model. Very similart to SpeedySpeech model but with pitch prediction. - - Paper:: - https://arxiv.org/abs/2006.06873 - - Paper abstract:: - We present FastPitch, a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental - frequency contours. The model predicts pitch contours during inference. By altering these predictions, - the generated speech can be more expressive, better match the semantic of the utterance, and in the end - more engaging to the listener. Uniformly increasing or decreasing pitch with FastPitch generates speech - that resembles the voluntary modulation of voice. Conditioning on frequency contours improves the overall - quality of synthesized speech, making it comparable to state-of-the-art. It does not introduce an overhead, - and FastPitch retains the favorable, fully-parallel Transformer architecture, with over 900x real-time - factor for mel-spectrogram synthesis of a typical utterance." - - Args: - config (Coqpit): Model coqpit class. - - Examples: - >>> from TTS.tts.models.fast_pitch import FastPitch, FastPitchArgs - >>> config = FastPitchArgs() - >>> model = FastPitch(config) - """ - - # pylint: disable=dangerous-default-value - def __init__(self, config: Coqpit): - - super().__init__() - - # don't use isintance not to import recursively - if config.__class__.__name__ == "FastPitchConfig": - if "characters" in config: - # loading from FasrPitchConfig - _, self.config, num_chars = self.get_characters(config) - config.model_args.num_chars = num_chars - self.args = self.config.model_args - else: - # loading from FastPitchArgs - self.config = config - self.args = config.model_args - elif isinstance(config, FastPitchArgs): - self.args = config - self.config = config - else: - raise ValueError("config must be either a VitsConfig or Vitsself.args") - - self.max_duration = self.args.max_duration - self.use_aligner = self.args.use_aligner - self.use_binary_alignment_loss = False - - self.length_scale = ( - float(self.args.length_scale) if isinstance(self.args.length_scale, int) else self.args.length_scale - ) - - self.emb = nn.Embedding(self.args.num_chars, self.args.hidden_channels) - - self.encoder = Encoder( - self.args.hidden_channels, - self.args.hidden_channels, - self.args.encoder_type, - self.args.encoder_params, - self.args.d_vector_dim, - ) - - if self.args.positional_encoding: - self.pos_encoder = PositionalEncoding(self.args.hidden_channels) - - self.decoder = Decoder( - self.args.out_channels, - self.args.hidden_channels, - self.args.decoder_type, - self.args.decoder_params, - ) - - self.duration_predictor = DurationPredictor( - self.args.hidden_channels + self.args.d_vector_dim, - self.args.duration_predictor_hidden_channels, - self.args.duration_predictor_kernel_size, - self.args.duration_predictor_dropout_p, - ) - - self.pitch_predictor = DurationPredictor( - self.args.hidden_channels + self.args.d_vector_dim, - self.args.pitch_predictor_hidden_channels, - self.args.pitch_predictor_kernel_size, - self.args.pitch_predictor_dropout_p, - ) - - self.pitch_emb = nn.Conv1d( - 1, - self.args.hidden_channels, - kernel_size=self.args.pitch_embedding_kernel_size, - padding=int((self.args.pitch_embedding_kernel_size - 1) / 2), - ) - - if self.args.num_speakers > 1 and not self.args.use_d_vector: - # speaker embedding layer - self.emb_g = nn.Embedding(self.args.num_speakers, self.args.d_vector_dim) - nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) - - if self.args.d_vector_dim > 0 and self.args.d_vector_dim != self.args.hidden_channels: - self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1) - - if self.args.use_aligner: - self.aligner = AlignmentNetwork( - in_query_channels=self.args.out_channels, in_key_channels=self.args.hidden_channels - ) - - @staticmethod - def generate_attn(dr, x_mask, y_mask=None): - """Generate an attention mask from the durations. - - Shapes - - dr: :math:`(B, T_{en})` - - x_mask: :math:`(B, T_{en})` - - y_mask: :math:`(B, T_{de})` - """ - # compute decode mask from the durations - if y_mask is None: - y_lengths = dr.sum(1).long() - y_lengths[y_lengths < 1] = 1 - y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype) - attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) - attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype) - return attn - - def expand_encoder_outputs(self, en, dr, x_mask, y_mask): - """Generate attention alignment map from durations and - expand encoder outputs - - Shapes - - en: :math:`(B, D_{en}, T_{en})` - - dr: :math:`(B, T_{en})` - - x_mask: :math:`(B, T_{en})` - - y_mask: :math:`(B, T_{de})` - - Examples: - - encoder output: :math:`[a,b,c,d]` - - durations: :math:`[1, 3, 2, 1]` - - - expanded: :math:`[a, b, b, b, c, c, d]` - - attention map: :math:`[[0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]]` - """ - attn = self.generate_attn(dr, x_mask, y_mask) - o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2).to(en.dtype), en.transpose(1, 2)).transpose(1, 2) - return o_en_ex, attn - - def format_durations(self, o_dr_log, x_mask): - """Format predicted durations. - 1. Convert to linear scale from log scale - 2. Apply the length scale for speed adjustment - 3. Apply masking. - 4. Cast 0 durations to 1. - 5. Round the duration values. - - Args: - o_dr_log: Log scale durations. - x_mask: Input text mask. - - Shapes: - - o_dr_log: :math:`(B, T_{de})` - - x_mask: :math:`(B, T_{en})` - """ - o_dr = (torch.exp(o_dr_log) - 1) * x_mask * self.length_scale - o_dr[o_dr < 1] = 1.0 - o_dr = torch.round(o_dr) - return o_dr - - @staticmethod - def _concat_speaker_embedding(o_en, g): - g_exp = g.expand(-1, -1, o_en.size(-1)) # [B, C, T_en] - o_en = torch.cat([o_en, g_exp], 1) - return o_en - - def _sum_speaker_embedding(self, x, g): - # project g to decoder dim. - if hasattr(self, "proj_g"): - g = self.proj_g(g) - return x + g - - def _forward_encoder( - self, x: torch.LongTensor, x_mask: torch.FloatTensor, g: torch.FloatTensor = None - ) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: - """Encoding forward pass. - - 1. Embed speaker IDs if multi-speaker mode. - 2. Embed character sequences. - 3. Run the encoder network. - 4. Concat speaker embedding to the encoder output for the duration predictor. - - Args: - x (torch.LongTensor): Input sequence IDs. - x_mask (torch.FloatTensor): Input squence mask. - g (torch.FloatTensor, optional): Conditioning vectors. In general speaker embeddings. Defaults to None. - - Returns: - Tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor]: - encoder output, encoder output for the duration predictor, input sequence mask, speaker embeddings, - character embeddings - - Shapes: - - x: :math:`(B, T_{en})` - - x_mask: :math:`(B, 1, T_{en})` - - g: :math:`(B, C)` - """ - if hasattr(self, "emb_g"): - g = nn.functional.normalize(self.emb_g(g)) # [B, C, 1] - if g is not None: - g = g.unsqueeze(-1) - # [B, T, C] - x_emb = self.emb(x) - # encoder pass - o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask) - # speaker conditioning for duration predictor - if g is not None: - o_en_dp = self._concat_speaker_embedding(o_en, g) - else: - o_en_dp = o_en - return o_en, o_en_dp, x_mask, g, x_emb - - def _forward_decoder( - self, - o_en: torch.FloatTensor, - dr: torch.IntTensor, - x_mask: torch.FloatTensor, - y_lengths: torch.IntTensor, - g: torch.FloatTensor, - ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: - """Decoding forward pass. - - 1. Compute the decoder output mask - 2. Expand encoder output with the durations. - 3. Apply position encoding. - 4. Add speaker embeddings if multi-speaker mode. - 5. Run the decoder. - - Args: - o_en (torch.FloatTensor): Encoder output. - dr (torch.IntTensor): Ground truth durations or alignment network durations. - x_mask (torch.IntTensor): Input sequence mask. - y_lengths (torch.IntTensor): Output sequence lengths. - g (torch.FloatTensor): Conditioning vectors. In general speaker embeddings. - - Returns: - Tuple[torch.FloatTensor, torch.FloatTensor]: Decoder output, attention map from durations. - """ - y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en.dtype) - # expand o_en with durations - o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask) - # positional encoding - if hasattr(self, "pos_encoder"): - o_en_ex = self.pos_encoder(o_en_ex, y_mask) - # speaker embedding - if g is not None: - o_en_ex = self._sum_speaker_embedding(o_en_ex, g) - # decoder pass - o_de = self.decoder(o_en_ex, y_mask, g=g) - return o_de.transpose(1, 2), attn.transpose(1, 2) - - def _forward_pitch_predictor( - self, - o_en: torch.FloatTensor, - x_mask: torch.IntTensor, - pitch: torch.FloatTensor = None, - dr: torch.IntTensor = None, - ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: - """Pitch predictor forward pass. - - 1. Predict pitch from encoder outputs. - 2. In training - Compute average pitch values for each input character from the ground truth pitch values. - 3. Embed average pitch values. - - Args: - o_en (torch.FloatTensor): Encoder output. - x_mask (torch.IntTensor): Input sequence mask. - pitch (torch.FloatTensor, optional): Ground truth pitch values. Defaults to None. - dr (torch.IntTensor, optional): Ground truth durations. Defaults to None. - - Returns: - Tuple[torch.FloatTensor, torch.FloatTensor]: Pitch embedding, pitch prediction. - - Shapes: - - o_en: :math:`(B, C, T_{en})` - - x_mask: :math:`(B, 1, T_{en})` - - pitch: :math:`(B, 1, T_{de})` - - dr: :math:`(B, T_{en})` - """ - o_pitch = self.pitch_predictor(o_en, x_mask) - if pitch is not None: - avg_pitch = average_over_durations(pitch, dr) - o_pitch_emb = self.pitch_emb(avg_pitch) - return o_pitch_emb, o_pitch, avg_pitch - o_pitch_emb = self.pitch_emb(o_pitch) - return o_pitch_emb, o_pitch - - def _forward_aligner( - self, x: torch.FloatTensor, y: torch.FloatTensor, x_mask: torch.IntTensor, y_mask: torch.IntTensor - ) -> Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: - """Aligner forward pass. - - 1. Compute a mask to apply to the attention map. - 2. Run the alignment network. - 3. Apply MAS to compute the hard alignment map. - 4. Compute the durations from the hard alignment map. - - Args: - x (torch.FloatTensor): Input sequence. - y (torch.FloatTensor): Output sequence. - x_mask (torch.IntTensor): Input sequence mask. - y_mask (torch.IntTensor): Output sequence mask. - - Returns: - Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: - Durations from the hard alignment map, soft alignment potentials, log scale alignment potentials, - hard alignment map. - - Shapes: - - x: :math:`[B, T_en, C_en]` - - y: :math:`[B, T_de, C_de]` - - x_mask: :math:`[B, 1, T_en]` - - y_mask: :math:`[B, 1, T_de]` - - - o_alignment_dur: :math:`[B, T_en]` - - alignment_soft: :math:`[B, T_en, T_de]` - - alignment_logprob: :math:`[B, 1, T_de, T_en]` - - alignment_mas: :math:`[B, T_en, T_de]` - """ - attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) - alignment_soft, alignment_logprob = self.aligner(y.transpose(1, 2), x.transpose(1, 2), x_mask, None) - alignment_mas = maximum_path( - alignment_soft.squeeze(1).transpose(1, 2).contiguous(), attn_mask.squeeze(1).contiguous() - ) - o_alignment_dur = torch.sum(alignment_mas, -1).int() - alignment_soft = alignment_soft.squeeze(1).transpose(1, 2) - return o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas - - def forward( - self, - x: torch.LongTensor, - x_lengths: torch.LongTensor, - y_lengths: torch.LongTensor, - y: torch.FloatTensor = None, - dr: torch.IntTensor = None, - pitch: torch.FloatTensor = None, - aux_input: Dict = {"d_vectors": 0, "speaker_ids": None}, # pylint: disable=unused-argument - ) -> Dict: - """Model's forward pass. - - Args: - x (torch.LongTensor): Input character sequences. - x_lengths (torch.LongTensor): Input sequence lengths. - y_lengths (torch.LongTensor): Output sequnce lengths. Defaults to None. - y (torch.FloatTensor): Spectrogram frames. Defaults to None. - dr (torch.IntTensor): Character durations over the spectrogram frames. Defaults to None. - pitch (torch.FloatTensor): Pitch values for each spectrogram frame. Defaults to None. - aux_input (Dict): Auxiliary model inputs. Defaults to `{"d_vectors": 0, "speaker_ids": None}`. - - Shapes: - - x: :math:`[B, T_max]` - - x_lengths: :math:`[B]` - - y_lengths: :math:`[B]` - - y: :math:`[B, T_max2]` - - dr: :math:`[B, T_max]` - - g: :math:`[B, C]` - - pitch: :math:`[B, 1, T]` - """ - g = aux_input["d_vectors"] if "d_vectors" in aux_input else None - # compute sequence masks - y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(y.dtype) - x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(y.dtype) - # encoder pass - o_en, o_en_dp, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g) - # duration predictor pass - if self.args.detach_duration_predictor: - o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask) - else: - o_dr_log = self.duration_predictor(o_en_dp, x_mask) - o_dr = torch.clamp(torch.exp(o_dr_log) - 1, 0, self.max_duration) - # generate attn mask from predicted durations - o_attn = self.generate_attn(o_dr.squeeze(1), x_mask) - # aligner pass - if self.use_aligner: - o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas = self._forward_aligner( - x_emb, y, x_mask, y_mask - ) - dr = o_alignment_dur - # pitch predictor pass - o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en_dp, x_mask, pitch, dr) - o_en = o_en + o_pitch_emb - # decoder pass - o_de, attn = self._forward_decoder(o_en, dr, x_mask, y_lengths, g=g) - outputs = { - "model_outputs": o_de, - "durations_log": o_dr_log.squeeze(1), - "durations": o_dr.squeeze(1), - "attn_durations": o_attn, # for visualization - "pitch_avg": o_pitch, - "pitch_avg_gt": avg_pitch, - "alignments": attn, - "alignment_soft": alignment_soft.transpose(1, 2), - "alignment_mas": alignment_mas.transpose(1, 2), - "o_alignment_dur": o_alignment_dur, - "alignment_logprob": alignment_logprob, - "x_mask": x_mask, - "y_mask": y_mask, - } - return outputs - - @torch.no_grad() - def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument - """Model's inference pass. - - Args: - x (torch.LongTensor): Input character sequence. - aux_input (Dict): Auxiliary model inputs. Defaults to `{"d_vectors": None, "speaker_ids": None}`. - - Shapes: - - x: [B, T_max] - - x_lengths: [B] - - g: [B, C] - """ - g = aux_input["d_vectors"] if "d_vectors" in aux_input else None - x_lengths = torch.tensor(x.shape[1:2]).to(x.device) - x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype).float() - # encoder pass - o_en, o_en_dp, x_mask, g, _ = self._forward_encoder(x, x_mask, g) - # duration predictor pass - o_dr_log = self.duration_predictor(o_en_dp, x_mask) - o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1) - y_lengths = o_dr.sum(1) - # pitch predictor pass - o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en_dp, x_mask) - o_en = o_en + o_pitch_emb - # decoder pass - o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=g) - outputs = { - "model_outputs": o_de, - "alignments": attn, - "pitch": o_pitch, - "durations_log": o_dr_log, - } - return outputs - - def train_step(self, batch: dict, criterion: nn.Module): - text_input = batch["text_input"] - text_lengths = batch["text_lengths"] - mel_input = batch["mel_input"] - mel_lengths = batch["mel_lengths"] - pitch = batch["pitch"] - d_vectors = batch["d_vectors"] - speaker_ids = batch["speaker_ids"] - durations = batch["durations"] - aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids} - - # forward pass - outputs = self.forward( - text_input, text_lengths, mel_lengths, y=mel_input, dr=durations, pitch=pitch, aux_input=aux_input - ) - # use aligner's output as the duration target - if self.use_aligner: - durations = outputs["o_alignment_dur"] - # use float32 in AMP - with autocast(enabled=False): - # compute loss - loss_dict = criterion( - decoder_output=outputs["model_outputs"], - decoder_target=mel_input, - decoder_output_lens=mel_lengths, - dur_output=outputs["durations_log"], - dur_target=durations, - pitch_output=outputs["pitch_avg"], - pitch_target=outputs["pitch_avg_gt"], - input_lens=text_lengths, - alignment_logprob=outputs["alignment_logprob"], - alignment_soft=outputs["alignment_soft"] if self.use_binary_alignment_loss else None, - alignment_hard=outputs["alignment_mas"] if self.use_binary_alignment_loss else None, - ) - # compute duration error - durations_pred = outputs["durations"] - duration_error = torch.abs(durations - durations_pred).sum() / text_lengths.sum() - loss_dict["duration_error"] = duration_error - - return outputs, loss_dict - - def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use - model_outputs = outputs["model_outputs"] - alignments = outputs["alignments"] - mel_input = batch["mel_input"] - pitch = batch["pitch"] - pitch_avg_expanded, _ = self.expand_encoder_outputs( - outputs["pitch_avg"], outputs["durations"], outputs["x_mask"], outputs["y_mask"] - ) - - pred_spec = model_outputs[0].data.cpu().numpy() - gt_spec = mel_input[0].data.cpu().numpy() - align_img = alignments[0].data.cpu().numpy() - pitch = pitch[0, 0].data.cpu().numpy() - - # TODO: denormalize before plotting - pitch = abs(pitch) - pitch_avg_expanded = abs(pitch_avg_expanded[0, 0]).data.cpu().numpy() - - figures = { - "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), - "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), - "alignment": plot_alignment(align_img, output_fig=False), - "pitch_ground_truth": plot_pitch(pitch, gt_spec, ap, output_fig=False), - "pitch_avg_predicted": plot_pitch(pitch_avg_expanded, pred_spec, ap, output_fig=False), - } - - # plot the attention mask computed from the predicted durations - if "attn_durations" in outputs: - alignments_hat = outputs["attn_durations"][0].data.cpu().numpy() - figures["alignment_hat"] = plot_alignment(alignments_hat.T, output_fig=False) - - # Sample audio - train_audio = ap.inv_melspectrogram(pred_spec.T) - return figures, {"audio": train_audio} - - def eval_step(self, batch: dict, criterion: nn.Module): - return self.train_step(batch, criterion) - - def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): - return self.train_log(ap, batch, outputs) - - def load_checkpoint( - self, config, checkpoint_path, eval=False - ): # pylint: disable=unused-argument, redefined-builtin - state = torch.load(checkpoint_path, map_location=torch.device("cpu")) - self.load_state_dict(state["model"]) - if eval: - self.eval() - assert not self.training - - def get_criterion(self): - from TTS.tts.layers.losses import FastPitchLoss # pylint: disable=import-outside-toplevel - - return FastPitchLoss(self.config) - - def on_train_step_start(self, trainer): - """Enable binary alignment loss when needed""" - if trainer.total_steps_done > self.config.binary_align_loss_start_step: - self.use_binary_alignment_loss = True diff --git a/TTS/tts/models/speedy_speech.py b/TTS/tts/models/speedy_speech.py deleted file mode 100644 index d85fa4dc..00000000 --- a/TTS/tts/models/speedy_speech.py +++ /dev/null @@ -1,320 +0,0 @@ -from dataclasses import dataclass, field - -import torch -from coqpit import Coqpit -from torch import nn - -from TTS.tts.layers.feed_forward.decoder import Decoder -from TTS.tts.layers.feed_forward.duration_predictor import DurationPredictor -from TTS.tts.layers.feed_forward.encoder import Encoder -from TTS.tts.layers.generic.pos_encoding import PositionalEncoding -from TTS.tts.utils.helpers import generate_path -from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.helpers import sequence_mask -from TTS.tts.utils.measures import alignment_diagonal_score -from TTS.tts.utils.visual import plot_alignment, plot_spectrogram -from TTS.utils.audio import AudioProcessor -from TTS.utils.io import load_fsspec - - -@dataclass -class SpeedySpeechArgs(Coqpit): - """ - Args: - num_chars (int): number of unique input to characters - out_channels (int): number of output tensor channels. It is equal to the expected spectrogram size. - hidden_channels (int): number of channels in all the model layers. - positional_encoding (bool, optional): enable/disable Positional encoding on encoder outputs. Defaults to True. - length_scale (int, optional): coefficient to set the speech speed. <1 slower, >1 faster. Defaults to 1. - encoder_type (str, optional): set the encoder type. Defaults to 'residual_conv_bn'. - encoder_params (dict, optional): set encoder parameters depending on 'encoder_type'. Defaults to { "kernel_size": 4, "dilations": 4 * [1, 2, 4] + [1], "num_conv_blocks": 2, "num_res_blocks": 13 }. - decoder_type (str, optional): decoder type. Defaults to 'residual_conv_bn'. - decoder_params (dict, optional): set decoder parameters depending on 'decoder_type'. Defaults to { "kernel_size": 4, "dilations": 4 * [1, 2, 4, 8] + [1], "num_conv_blocks": 2, "num_res_blocks": 17 }. - num_speakers (int, optional): number of speakers for multi-speaker training. Defaults to 0. - use_d_vector (bool, optional): enable external speaker embeddings. Defaults to False. - d_vector_dim (int, optional): number of channels in speaker embedding vectors. Defaults to 0. - """ - - num_chars: int = None - out_channels: int = 80 - hidden_channels: int = 128 - num_speakers: int = 0 - positional_encoding: bool = True - length_scale: int = 1 - encoder_type: str = "residual_conv_bn" - encoder_params: dict = field( - default_factory=lambda: { - "kernel_size": 4, - "dilations": 4 * [1, 2, 4] + [1], - "num_conv_blocks": 2, - "num_res_blocks": 13, - } - ) - decoder_type: str = "residual_conv_bn" - decoder_params: dict = field( - default_factory=lambda: { - "kernel_size": 4, - "dilations": 4 * [1, 2, 4, 8] + [1], - "num_conv_blocks": 2, - "num_res_blocks": 17, - } - ) - use_d_vector: bool = False - d_vector_dim: int = 0 - - -class SpeedySpeech(BaseTTS): - """Speedy Speech model - https://arxiv.org/abs/2008.03802 - - Encoder -> DurationPredictor -> Decoder - - Paper abstract: - While recent neural sequence-to-sequence models have greatly improved the quality of speech - synthesis, there has not been a system capable of fast training, fast inference and high-quality audio synthesis - at the same time. We propose a student-teacher network capable of high-quality faster-than-real-time spectrogram - synthesis, with low requirements on computational resources and fast training time. We show that self-attention - layers are not necessary for generation of high quality audio. We utilize simple convolutional blocks with - residual connections in both student and teacher networks and use only a single attention layer in the teacher - model. Coupled with a MelGAN vocoder, our model's voice quality was rated significantly higher than Tacotron 2. - Our model can be efficiently trained on a single GPU and can run in real time even on a CPU. We provide both - our source code and audio samples in our GitHub repository. - - Notes: - The vanilla model is able to achieve a reasonable performance with only - ~3M model parameters and convolutional layers. - - This model requires precomputed phoneme durations to train a duration predictor. At inference - it only uses the duration predictor to compute durations and expand encoder outputs respectively. - - You can also mix and match different encoder and decoder networks beyond the paper. - - Check `SpeedySpeechArgs` for arguments. - """ - - # pylint: disable=dangerous-default-value - - def __init__(self, config: Coqpit): - super().__init__() - self.config = config - - if "characters" in config: - _, self.config, self.num_chars = self.get_characters(config) - - self.length_scale = ( - float(config.model_args.length_scale) - if isinstance(config.model_args.length_scale, int) - else config.model_args.length_scale - ) - self.emb = nn.Embedding(self.num_chars, config.model_args.hidden_channels) - self.encoder = Encoder( - config.model_args.hidden_channels, - config.model_args.hidden_channels, - config.model_args.encoder_type, - config.model_args.encoder_params, - config.model_args.d_vector_dim, - ) - if config.model_args.positional_encoding: - self.pos_encoder = PositionalEncoding(config.model_args.hidden_channels) - self.decoder = Decoder( - config.model_args.out_channels, - config.model_args.hidden_channels, - config.model_args.decoder_type, - config.model_args.decoder_params, - ) - self.duration_predictor = DurationPredictor(config.model_args.hidden_channels + config.model_args.d_vector_dim) - - if config.model_args.num_speakers > 1 and not config.model_args.use_d_vector: - # speaker embedding layer - self.emb_g = nn.Embedding(config.model_args.num_speakers, config.model_args.d_vector_dim) - nn.init.uniform_(self.emb_g.weight, -0.1, 0.1) - - if config.model_args.d_vector_dim > 0 and config.model_args.d_vector_dim != config.model_args.hidden_channels: - self.proj_g = nn.Conv1d(config.model_args.d_vector_dim, config.model_args.hidden_channels, 1) - - @staticmethod - def expand_encoder_outputs(en, dr, x_mask, y_mask): - """Generate attention alignment map from durations and - expand encoder outputs - - Example: - encoder output: [a,b,c,d] - durations: [1, 3, 2, 1] - - expanded: [a, b, b, b, c, c, d] - attention map: [[0, 0, 0, 0, 0, 0, 1], - [0, 0, 0, 0, 1, 1, 0], - [0, 1, 1, 1, 0, 0, 0], - [1, 0, 0, 0, 0, 0, 0]] - """ - attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) - attn = generate_path(dr, attn_mask.squeeze(1)).to(en.dtype) - o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2), en.transpose(1, 2)).transpose(1, 2) - return o_en_ex, attn - - def format_durations(self, o_dr_log, x_mask): - o_dr = (torch.exp(o_dr_log) - 1) * x_mask * self.length_scale - o_dr[o_dr < 1] = 1.0 - o_dr = torch.round(o_dr) - return o_dr - - @staticmethod - def _concat_speaker_embedding(o_en, g): - g_exp = g.expand(-1, -1, o_en.size(-1)) # [B, C, T_en] - o_en = torch.cat([o_en, g_exp], 1) - return o_en - - def _sum_speaker_embedding(self, x, g): - # project g to decoder dim. - if hasattr(self, "proj_g"): - g = self.proj_g(g) - return x + g - - def _forward_encoder(self, x, x_lengths, g=None): - if hasattr(self, "emb_g"): - g = nn.functional.normalize(self.emb_g(g)) # [B, C, 1] - - if g is not None: - g = g.unsqueeze(-1) - - # [B, T, C] - x_emb = self.emb(x) - # [B, C, T] - x_emb = torch.transpose(x_emb, 1, -1) - - # compute sequence masks - x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype) - - # encoder pass - o_en = self.encoder(x_emb, x_mask) - - # speaker conditioning for duration predictor - if g is not None: - o_en_dp = self._concat_speaker_embedding(o_en, g) - else: - o_en_dp = o_en - return o_en, o_en_dp, x_mask, g - - def _forward_decoder(self, o_en, o_en_dp, dr, x_mask, y_lengths, g): - y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en_dp.dtype) - # expand o_en with durations - o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask) - # positional encoding - if hasattr(self, "pos_encoder"): - o_en_ex = self.pos_encoder(o_en_ex, y_mask) - # speaker embedding - if g is not None: - o_en_ex = self._sum_speaker_embedding(o_en_ex, g) - # decoder pass - o_de = self.decoder(o_en_ex, y_mask, g=g) - return o_de, attn.transpose(1, 2) - - def forward( - self, x, x_lengths, y_lengths, dr, aux_input={"d_vectors": None, "speaker_ids": None} - ): # pylint: disable=unused-argument - """ - TODO: speaker embedding for speaker_ids - Shapes: - x: [B, T_max] - x_lengths: [B] - y_lengths: [B] - dr: [B, T_max] - g: [B, C] - """ - g = aux_input["d_vectors"] if "d_vectors" in aux_input else None - o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g) - o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask) - o_de, attn = self._forward_decoder(o_en, o_en_dp, dr, x_mask, y_lengths, g=g) - outputs = {"model_outputs": o_de.transpose(1, 2), "durations_log": o_dr_log.squeeze(1), "alignments": attn} - return outputs - - @torch.no_grad() - def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument - """ - Shapes: - x: [B, T_max] - x_lengths: [B] - g: [B, C] - """ - g = aux_input["d_vectors"] if "d_vectors" in aux_input else None - x_lengths = torch.tensor(x.shape[1:2]).to(x.device) - # input sequence should be greated than the max convolution size - inference_padding = 5 - if x.shape[1] < 13: - inference_padding += 13 - x.shape[1] - # pad input to prevent dropping the last word - x = torch.nn.functional.pad(x, pad=(0, inference_padding), mode="constant", value=0) - o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g) - # duration predictor pass - o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask) - o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1) - y_lengths = o_dr.sum(1) - o_de, attn = self._forward_decoder(o_en, o_en_dp, o_dr, x_mask, y_lengths, g=g) - outputs = {"model_outputs": o_de.transpose(1, 2), "alignments": attn, "durations_log": None} - return outputs - - def train_step(self, batch: dict, criterion: nn.Module): - text_input = batch["text_input"] - text_lengths = batch["text_lengths"] - mel_input = batch["mel_input"] - mel_lengths = batch["mel_lengths"] - d_vectors = batch["d_vectors"] - speaker_ids = batch["speaker_ids"] - durations = batch["durations"] - - aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids} - outputs = self.forward(text_input, text_lengths, mel_lengths, durations, aux_input) - - # compute loss - loss_dict = criterion( - outputs["model_outputs"], - mel_input, - mel_lengths, - outputs["durations_log"], - torch.log(1 + durations), - text_lengths, - ) - - # compute alignment error (the lower the better ) - align_error = 1 - alignment_diagonal_score(outputs["alignments"], binary=True) - loss_dict["align_error"] = align_error - return outputs, loss_dict - - def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use - model_outputs = outputs["model_outputs"] - alignments = outputs["alignments"] - mel_input = batch["mel_input"] - - pred_spec = model_outputs[0].data.cpu().numpy() - gt_spec = mel_input[0].data.cpu().numpy() - align_img = alignments[0].data.cpu().numpy() - - figures = { - "prediction": plot_spectrogram(pred_spec, ap, output_fig=False), - "ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False), - "alignment": plot_alignment(align_img, output_fig=False), - } - - # Sample audio - train_audio = ap.inv_melspectrogram(pred_spec.T) - return figures, {"audio": train_audio} - - def eval_step(self, batch: dict, criterion: nn.Module): - return self.train_step(batch, criterion) - - def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict): - return self.train_log(ap, batch, outputs) - - def load_checkpoint( - self, config, checkpoint_path, eval=False - ): # pylint: disable=unused-argument, redefined-builtin - state = load_fsspec(checkpoint_path, map_location=torch.device("cpu")) - self.load_state_dict(state["model"]) - if eval: - self.eval() - assert not self.training - - def get_criterion(self): - from TTS.tts.layers.losses import SpeedySpeechLoss # pylint: disable=import-outside-toplevel - - return SpeedySpeechLoss(self.config) From 3abc3a1d3254547a640a1ee0353c89ef33faf021 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:28:10 +0000 Subject: [PATCH 15/33] Fix GPU init in tests --- tests/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/__init__.py b/tests/__init__.py index 2b07004f..45aee23a 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -7,8 +7,8 @@ from TTS.utils.generic_utils import get_cuda def get_device_id(): use_cuda, _ = get_cuda() if use_cuda: - if 'CUDA_VISIBLE_DEVICES' in os.environ and os.environ['CUDA_VISIBLE_DEVICES'] != "": - GPU_ID = os.environ['CUDA_VISIBLE_DEVICES'].split(',')[0] + if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] != "": + GPU_ID = os.environ["CUDA_VISIBLE_DEVICES"].split(",")[0] else: GPU_ID = "0" else: From 570d5971be3866be9c19e914464b8156b478df9a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:29:12 +0000 Subject: [PATCH 16/33] Implement `ForwardTTSLoss` --- TTS/tts/layers/losses.py | 121 ++++++++++++++++++++------------------- 1 file changed, 61 insertions(+), 60 deletions(-) diff --git a/TTS/tts/layers/losses.py b/TTS/tts/layers/losses.py index 2c752376..72e7d8d5 100644 --- a/TTS/tts/layers/losses.py +++ b/TTS/tts/layers/losses.py @@ -236,10 +236,40 @@ class Huber(nn.Module): y: B x T length: B """ - mask = sequence_mask(sequence_length=length, max_len=y.size(1)).float() + mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2).float() return torch.nn.functional.smooth_l1_loss(x * mask, y * mask, reduction="sum") / mask.sum() +class ForwardSumLoss(nn.Module): + def __init__(self, blank_logprob=-1): + super().__init__() + self.log_softmax = torch.nn.LogSoftmax(dim=3) + self.ctc_loss = torch.nn.CTCLoss(zero_infinity=True) + self.blank_logprob = blank_logprob + + def forward(self, attn_logprob, in_lens, out_lens): + key_lens = in_lens + query_lens = out_lens + attn_logprob_padded = torch.nn.functional.pad(input=attn_logprob, pad=(1, 0), value=self.blank_logprob) + + total_loss = 0.0 + for bid in range(attn_logprob.shape[0]): + target_seq = torch.arange(1, key_lens[bid] + 1).unsqueeze(0) + curr_logprob = attn_logprob_padded[bid].permute(1, 0, 2)[: query_lens[bid], :, : key_lens[bid] + 1] + + curr_logprob = self.log_softmax(curr_logprob[None])[0] + loss = self.ctc_loss( + curr_logprob, + target_seq, + input_lengths=query_lens[bid : bid + 1], + target_lengths=key_lens[bid : bid + 1], + ) + total_loss = total_loss + loss + + total_loss = total_loss / attn_logprob.shape[0] + return total_loss + + ######################## # MODEL LOSS LAYERS ######################## @@ -413,25 +443,6 @@ class GlowTTSLoss(torch.nn.Module): return return_dict -class SpeedySpeechLoss(nn.Module): - def __init__(self, c): - super().__init__() - self.l1 = L1LossMasked(False) - self.ssim = SSIMLoss() - self.huber = Huber() - - self.ssim_alpha = c.ssim_alpha - self.huber_alpha = c.huber_alpha - self.l1_alpha = c.l1_alpha - - def forward(self, decoder_output, decoder_target, decoder_output_lens, dur_output, dur_target, input_lens): - l1_loss = self.l1(decoder_output, decoder_target, decoder_output_lens) - ssim_loss = self.ssim(decoder_output, decoder_target, decoder_output_lens) - huber_loss = self.huber(dur_output, dur_target, input_lens) - loss = self.l1_alpha * l1_loss + self.ssim_alpha * ssim_loss + self.huber_alpha * huber_loss - return {"loss": loss, "loss_l1": l1_loss, "loss_ssim": ssim_loss, "loss_dur": huber_loss} - - def mse_loss_custom(x, y): """MSE loss using the torch back-end without reduction. It uses less VRAM than the raw code""" @@ -660,51 +671,41 @@ class VitsDiscriminatorLoss(nn.Module): return return_dict -class ForwardSumLoss(nn.Module): - def __init__(self, blank_logprob=-1): - super().__init__() - self.log_softmax = torch.nn.LogSoftmax(dim=3) - self.ctc_loss = torch.nn.CTCLoss(zero_infinity=True) - self.blank_logprob = blank_logprob +class ForwardTTSLoss(nn.Module): + """Generic configurable ForwardTTS loss.""" - def forward(self, attn_logprob, in_lens, out_lens): - key_lens = in_lens - query_lens = out_lens - attn_logprob_padded = torch.nn.functional.pad(input=attn_logprob, pad=(1, 0), value=self.blank_logprob) - - total_loss = 0.0 - for bid in range(attn_logprob.shape[0]): - target_seq = torch.arange(1, key_lens[bid] + 1).unsqueeze(0) - curr_logprob = attn_logprob_padded[bid].permute(1, 0, 2)[: query_lens[bid], :, : key_lens[bid] + 1] - - curr_logprob = self.log_softmax(curr_logprob[None])[0] - loss = self.ctc_loss( - curr_logprob, - target_seq, - input_lengths=query_lens[bid : bid + 1], - target_lengths=key_lens[bid : bid + 1], - ) - total_loss = total_loss + loss - - total_loss = total_loss / attn_logprob.shape[0] - return total_loss - - -class FastPitchLoss(nn.Module): def __init__(self, c): super().__init__() - self.spec_loss = MSELossMasked(False) - self.ssim = SSIMLoss() - self.dur_loss = MSELossMasked(False) - self.pitch_loss = MSELossMasked(False) + if c.spec_loss_type == "mse": + self.spec_loss = MSELossMasked(False) + elif c.spec_loss_type == "l1": + self.spec_loss = L1LossMasked(False) + else: + raise ValueError(" [!] Unknown spec_loss_type {}".format(c.spec_loss_type)) + + if c.duration_loss_type == "mse": + self.dur_loss = MSELossMasked(False) + elif c.duration_loss_type == "l1": + self.dur_loss = L1LossMasked(False) + elif c.duration_loss_type == "huber": + self.dur_loss = Huber() + else: + raise ValueError(" [!] Unknown duration_loss_type {}".format(c.duration_loss_type)) + if c.model_args.use_aligner: self.aligner_loss = ForwardSumLoss() + self.aligner_loss_alpha = c.aligner_loss_alpha + + if c.model_args.use_pitch: + self.pitch_loss = MSELossMasked(False) + self.pitch_loss_alpha = c.pitch_loss_alpha + + if c.use_ssim_loss: + self.ssim = SSIMLoss() if c.use_ssim_loss else None + self.ssim_loss_alpha = c.ssim_loss_alpha self.spec_loss_alpha = c.spec_loss_alpha - self.ssim_loss_alpha = c.ssim_loss_alpha self.dur_loss_alpha = c.dur_loss_alpha - self.pitch_loss_alpha = c.pitch_loss_alpha - self.aligner_loss_alpha = c.aligner_loss_alpha self.binary_alignment_loss_alpha = c.binary_align_loss_alpha @staticmethod @@ -731,7 +732,7 @@ class FastPitchLoss(nn.Module): ): loss = 0 return_dict = {} - if self.ssim_loss_alpha > 0: + if hasattr(self, "ssim_loss") and self.ssim_loss_alpha > 0: ssim_loss = self.ssim(decoder_output, decoder_target, decoder_output_lens) loss = loss + self.ssim_loss_alpha * ssim_loss return_dict["loss_ssim"] = self.ssim_loss_alpha * ssim_loss @@ -747,12 +748,12 @@ class FastPitchLoss(nn.Module): loss = loss + self.dur_loss_alpha * dur_loss return_dict["loss_dur"] = self.dur_loss_alpha * dur_loss - if self.pitch_loss_alpha > 0: + if hasattr(self, "pitch_loss") and self.pitch_loss_alpha > 0: pitch_loss = self.pitch_loss(pitch_output.transpose(1, 2), pitch_target.transpose(1, 2), input_lens) loss = loss + self.pitch_loss_alpha * pitch_loss return_dict["loss_pitch"] = self.pitch_loss_alpha * pitch_loss - if self.aligner_loss_alpha > 0: + if hasattr(self, "aligner_loss") and self.aligner_loss_alpha > 0: aligner_loss = self.aligner_loss(alignment_logprob, input_lens, decoder_output_lens) loss = loss + self.aligner_loss_alpha * aligner_loss return_dict["loss_aligner"] = self.aligner_loss_alpha * aligner_loss From a89eb12acab8d0ce7046cdcba38d8ab51fd9eed6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:29:51 +0000 Subject: [PATCH 17/33] Fix glow_tts imports --- TTS/tts/models/glow_tts.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/TTS/tts/models/glow_tts.py b/TTS/tts/models/glow_tts.py index 2f52b363..e643c69f 100644 --- a/TTS/tts/models/glow_tts.py +++ b/TTS/tts/models/glow_tts.py @@ -7,9 +7,8 @@ from torch.nn import functional as F from TTS.tts.configs import GlowTTSConfig from TTS.tts.layers.glow_tts.decoder import Decoder from TTS.tts.layers.glow_tts.encoder import Encoder -from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.helpers import sequence_mask +from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask from TTS.tts.utils.speakers import get_speaker_manager from TTS.tts.utils.synthesis import synthesis from TTS.tts.utils.visual import plot_alignment, plot_spectrogram @@ -133,7 +132,7 @@ class GlowTTS(BaseTTS): return y_mean, y_log_scale, o_attn_dur def forward( - self, x, x_lengths, y, y_lengths=None, aux_input={"d_vectors": None, 'speaker_ids':None} + self, x, x_lengths, y, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None} ): # pylint: disable=dangerous-default-value """ Shapes: @@ -185,7 +184,7 @@ class GlowTTS(BaseTTS): @torch.no_grad() def inference_with_MAS( - self, x, x_lengths, y=None, y_lengths=None, aux_input={"d_vectors": None, 'speaker_ids':None} + self, x, x_lengths, y=None, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None} ): # pylint: disable=dangerous-default-value """ It's similar to the teacher forcing in Tacotron. @@ -246,7 +245,7 @@ class GlowTTS(BaseTTS): @torch.no_grad() def decoder_inference( - self, y, y_lengths=None, aux_input={"d_vectors": None, 'speaker_ids':None} + self, y, y_lengths=None, aux_input={"d_vectors": None, "speaker_ids": None} ): # pylint: disable=dangerous-default-value """ Shapes: @@ -278,7 +277,9 @@ class GlowTTS(BaseTTS): return outputs @torch.no_grad() - def inference(self, x, aux_input={"x_lengths": None, "d_vectors": None, "speaker_ids":None}): # pylint: disable=dangerous-default-value + def inference( + self, x, aux_input={"x_lengths": None, "d_vectors": None, "speaker_ids": None} + ): # pylint: disable=dangerous-default-value x_lengths = aux_input["x_lengths"] g = aux_input["d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None @@ -331,7 +332,13 @@ class GlowTTS(BaseTTS): d_vectors = batch["d_vectors"] speaker_ids = batch["speaker_ids"] - outputs = self.forward(text_input, text_lengths, mel_input, mel_lengths, aux_input={"d_vectors": d_vectors, "speaker_ids":speaker_ids}) + outputs = self.forward( + text_input, + text_lengths, + mel_input, + mel_lengths, + aux_input={"d_vectors": d_vectors, "speaker_ids": speaker_ids}, + ) loss_dict = criterion( outputs["model_outputs"], From d6e29ef98a11975fbf5d78c277c0f8c96a30bd8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:30:33 +0000 Subject: [PATCH 18/33] Style update --- TTS/tts/layers/generic/transformer.py | 4 +- TTS/tts/utils/data.py | 2 - tests/data_tests/test_loader.py | 66 +++++++++++++-------------- 3 files changed, 36 insertions(+), 36 deletions(-) diff --git a/TTS/tts/layers/generic/transformer.py b/TTS/tts/layers/generic/transformer.py index 2fe9bcc4..9b7ecee2 100644 --- a/TTS/tts/layers/generic/transformer.py +++ b/TTS/tts/layers/generic/transformer.py @@ -70,7 +70,9 @@ class FFTransformerBlock(nn.Module): class FFTDurationPredictor: - def __init__(self, in_channels, hidden_channels, num_heads, num_layers, dropout_p=0.1, cond_channels=None): # pylint: disable=unused-argument + def __init__( + self, in_channels, hidden_channels, num_heads, num_layers, dropout_p=0.1, cond_channels=None + ): # pylint: disable=unused-argument self.fft = FFTransformerBlock(in_channels, num_heads, hidden_channels, num_layers, dropout_p) self.proj = nn.Linear(in_channels, 1) diff --git a/TTS/tts/utils/data.py b/TTS/tts/utils/data.py index 7a766958..d91a828e 100644 --- a/TTS/tts/utils/data.py +++ b/TTS/tts/utils/data.py @@ -52,5 +52,3 @@ def prepare_stop_target(inputs, out_steps): def pad_per_step(inputs, pad_len): return np.pad(inputs, [[0, 0], [0, 0], [0, pad_len]], mode="constant", constant_values=0.0) - - diff --git a/tests/data_tests/test_loader.py b/tests/data_tests/test_loader.py index 0fbb6bde..18066ef3 100644 --- a/tests/data_tests/test_loader.py +++ b/tests/data_tests/test_loader.py @@ -68,15 +68,15 @@ class TestTTSDataset(unittest.TestCase): for i, data in enumerate(dataloader): if i == self.max_loader_iter: break - text_input = data['text'] - text_lengths = data['text_lengths'] - speaker_name = data['speaker_names'] - linear_input = data['linear'] - mel_input = data['mel'] - mel_lengths = data['mel_lengths'] - stop_target = data['stop_targets'] - item_idx = data['item_idxs'] - wavs = data['waveform'] + text_input = data["text"] + text_lengths = data["text_lengths"] + speaker_name = data["speaker_names"] + linear_input = data["linear"] + mel_input = data["mel"] + mel_lengths = data["mel_lengths"] + stop_target = data["stop_targets"] + item_idx = data["item_idxs"] + wavs = data["waveform"] neg_values = text_input[text_input < 0] check_count = len(neg_values) @@ -113,14 +113,14 @@ class TestTTSDataset(unittest.TestCase): for i, data in enumerate(dataloader): if i == self.max_loader_iter: break - text_input = data['text'] - text_lengths = data['text_lengths'] - speaker_name = data['speaker_names'] - linear_input = data['linear'] - mel_input = data['mel'] - mel_lengths = data['mel_lengths'] - stop_target = data['stop_targets'] - item_idx = data['item_idxs'] + text_input = data["text"] + text_lengths = data["text_lengths"] + speaker_name = data["speaker_names"] + linear_input = data["linear"] + mel_input = data["mel"] + mel_lengths = data["mel_lengths"] + stop_target = data["stop_targets"] + item_idx = data["item_idxs"] avg_length = mel_lengths.numpy().mean() assert avg_length >= last_length @@ -139,14 +139,14 @@ class TestTTSDataset(unittest.TestCase): for i, data in enumerate(dataloader): if i == self.max_loader_iter: break - text_input = data['text'] - text_lengths = data['text_lengths'] - speaker_name = data['speaker_names'] - linear_input = data['linear'] - mel_input = data['mel'] - mel_lengths = data['mel_lengths'] - stop_target = data['stop_targets'] - item_idx = data['item_idxs'] + text_input = data["text"] + text_lengths = data["text_lengths"] + speaker_name = data["speaker_names"] + linear_input = data["linear"] + mel_input = data["mel"] + mel_lengths = data["mel_lengths"] + stop_target = data["stop_targets"] + item_idx = data["item_idxs"] # check mel_spec consistency wav = np.asarray(self.ap.load_wav(item_idx[0]), dtype=np.float32) @@ -188,14 +188,14 @@ class TestTTSDataset(unittest.TestCase): for i, data in enumerate(dataloader): if i == self.max_loader_iter: break - text_input = data['text'] - text_lengths = data['text_lengths'] - speaker_name = data['speaker_names'] - linear_input = data['linear'] - mel_input = data['mel'] - mel_lengths = data['mel_lengths'] - stop_target = data['stop_targets'] - item_idx = data['item_idxs'] + text_input = data["text"] + text_lengths = data["text_lengths"] + speaker_name = data["speaker_names"] + linear_input = data["linear"] + mel_input = data["mel"] + mel_lengths = data["mel_lengths"] + stop_target = data["stop_targets"] + item_idx = data["item_idxs"] if mel_lengths[0] > mel_lengths[1]: idx = 0 From 22822cd41c9e3f962863825d22b3250024263ac5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 08:31:10 +0000 Subject: [PATCH 19/33] Add LJSpeech SpeedySpeech recipe --- .../speedy_speech/train_speedy_speech.py | 68 +++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 recipes/ljspeech/speedy_speech/train_speedy_speech.py diff --git a/recipes/ljspeech/speedy_speech/train_speedy_speech.py b/recipes/ljspeech/speedy_speech/train_speedy_speech.py new file mode 100644 index 00000000..2882468f --- /dev/null +++ b/recipes/ljspeech/speedy_speech/train_speedy_speech.py @@ -0,0 +1,68 @@ +import os + +from TTS.config import BaseAudioConfig, BaseDatasetConfig +from TTS.trainer import Trainer, TrainingArgs, init_training +from TTS.tts.configs import SpeedySpeechConfig +from TTS.utils.manage import ModelManager + +output_path = os.path.dirname(os.path.abspath(__file__)) + +# init configs +dataset_config = BaseDatasetConfig( + name="ljspeech", + meta_file_train="metadata.csv", + # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"), + path=os.path.join(output_path, "../LJSpeech-1.1/"), +) + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=60.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = SpeedySpeechConfig( + run_name="speedy_speech_ljspeech", + audio=audio_config, + batch_size=32, + eval_batch_size=16, + num_loader_workers=4, + num_eval_loader_workers=4, + compute_input_seq_cache=True, + run_eval=True, + test_delay_epochs=-1, + epochs=1000, + text_cleaner="english_cleaners", + use_phonemes=True, + use_espeak_phonemes=False, + phoneme_language="en-us", + phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), + print_step=50, + print_eval=False, + mixed_precision=False, + sort_by_audio_len=True, + max_seq_len=500000, + output_path=output_path, + datasets=[dataset_config], +) + +# compute alignments +if not config.model_args.use_aligner: + manager = ModelManager() + model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA") + # TODO: make compute_attention python callable + os.system( + f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true" + ) + +# train the model +args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) +trainer = Trainer(args, config, output_path, c_logger, tb_logger) +trainer.fit() From 66732025e113cfdc0d35d426873d86285451c110 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:23:48 +0000 Subject: [PATCH 20/33] Add `base_model` field to `forward_tts` configs --- TTS/trainer.py | 1 - TTS/tts/configs/fast_pitch_config.py | 10 ++++++++-- TTS/tts/configs/speedy_speech_config.py | 9 +++++++-- TTS/tts/models/__init__.py | 6 +++++- 4 files changed, 20 insertions(+), 6 deletions(-) diff --git a/TTS/trainer.py b/TTS/trainer.py index 2f3e9bb1..d90c5473 100644 --- a/TTS/trainer.py +++ b/TTS/trainer.py @@ -1182,7 +1182,6 @@ def process_args(args, config=None): args.restore_path, best_model = get_last_checkpoint(args.continue_path) if not args.best_path: args.best_path = best_model - # init config if not already defined if config is None: if args.config_path: diff --git a/TTS/tts/configs/fast_pitch_config.py b/TTS/tts/configs/fast_pitch_config.py index 3840d1d6..668ea227 100644 --- a/TTS/tts/configs/fast_pitch_config.py +++ b/TTS/tts/configs/fast_pitch_config.py @@ -18,6 +18,10 @@ class FastPitchConfig(BaseTTSConfig): model (str): Model name used for selecting the right model at initialization. Defaults to `fast_pitch`. + base_model (str): + Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate + the base model rather than searching for the `model` implementation. Defaults to `forward_tts`. + model_args (Coqpit): Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`. @@ -94,9 +98,11 @@ class FastPitchConfig(BaseTTSConfig): Maximum input sequence length to be used at training. Larger values result in more VRAM usage. """ - model: str = "forward_tts" + model: str = "fast_pitch" + base_model: str = "forward_tts" + # model specific params - model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs) + model_args: ForwardTTSArgs = ForwardTTSArgs() # multi-speaker settings use_speaker_embedding: bool = False diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py index bdfc2a6b..1ec8f729 100644 --- a/TTS/tts/configs/speedy_speech_config.py +++ b/TTS/tts/configs/speedy_speech_config.py @@ -16,7 +16,11 @@ class SpeedySpeechConfig(BaseTTSConfig): Args: model (str): - Model name used for selecting the right model at initialization. Defaults to `fast_pitch`. + Model name used for selecting the right model at initialization. Defaults to `speedy_speech`. + + base_model (str): + Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate + the base model rather than searching for the `model` implementation. Defaults to `forward_tts`. model_args (Coqpit): Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`. @@ -91,7 +95,8 @@ class SpeedySpeechConfig(BaseTTSConfig): Maximum input sequence length to be used at training. Larger values result in more VRAM usage. """ - model: str = "forward_tts" + model: str = "speedy_speech" + base_model: str = "forward_tts" # set model args as SpeedySpeech model_args: ForwardTTSArgs = ForwardTTSArgs( diff --git a/TTS/tts/models/__init__.py b/TTS/tts/models/__init__.py index 8c1bd430..1236fa76 100644 --- a/TTS/tts/models/__init__.py +++ b/TTS/tts/models/__init__.py @@ -4,7 +4,11 @@ from TTS.utils.generic_utils import find_module def setup_model(config): print(" > Using model: {}".format(config.model)) - MyModel = find_module("TTS.tts.models", config.model.lower()) + # fetch the right model implementation. + if "base_model" in config and config["base_model"] is not None: + MyModel = find_module("TTS.tts.models", config.base_model.lower()) + else: + MyModel = find_module("TTS.tts.models", config.model.lower()) # define set of characters used by the model if config.characters is not None: # set characters from config From ab37fa9c3905f9e0bd3c0f73c423e03ab8265ff0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:25:00 +0000 Subject: [PATCH 21/33] Edit AlignTTS --- TTS/tts/models/align_tts.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/TTS/tts/models/align_tts.py b/TTS/tts/models/align_tts.py index 0d75f482..78fbaeab 100644 --- a/TTS/tts/models/align_tts.py +++ b/TTS/tts/models/align_tts.py @@ -10,9 +10,8 @@ from TTS.tts.layers.feed_forward.decoder import Decoder from TTS.tts.layers.feed_forward.duration_predictor import DurationPredictor from TTS.tts.layers.feed_forward.encoder import Encoder from TTS.tts.layers.generic.pos_encoding import PositionalEncoding -from TTS.tts.utils.helpers import generate_path, maximum_path from TTS.tts.models.base_tts import BaseTTS -from TTS.tts.utils.helpers import sequence_mask +from TTS.tts.utils.helpers import generate_path, maximum_path, sequence_mask from TTS.tts.utils.visual import plot_alignment, plot_spectrogram from TTS.utils.audio import AudioProcessor from TTS.utils.io import load_fsspec @@ -168,7 +167,12 @@ class AlignTTS(BaseTTS): return dr_mas.squeeze(1), log_p @staticmethod - def convert_dr_to_align(dr, x_mask, y_mask): + def generate_attn(dr, x_mask, y_mask=None): + # compute decode mask from the durations + if y_mask is None: + y_lengths = dr.sum(1).long() + y_lengths[y_lengths < 1] = 1 + y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype) attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2) attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype) return attn @@ -187,7 +191,7 @@ class AlignTTS(BaseTTS): [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]] """ - attn = self.convert_dr_to_align(dr, x_mask, y_mask) + attn = self.generate_attn(dr, x_mask, y_mask) o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2), en.transpose(1, 2)).transpose(1, 2) return o_en_ex, attn @@ -275,7 +279,7 @@ class AlignTTS(BaseTTS): o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g) dr_mas, mu, log_sigma, logp = self._forward_mdn(o_en, y, y_lengths, x_mask) y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en_dp.dtype) - attn = self.convert_dr_to_align(dr_mas, x_mask, y_mask) + attn = self.generate_attn(dr_mas, x_mask, y_mask) elif phase == 1: # train decoder o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g) From d5f256b34cd69fe20b259ab15542a2d2590619df Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:26:23 +0000 Subject: [PATCH 22/33] Update tacotron `r` init --- TTS/tts/models/base_tacotron.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/TTS/tts/models/base_tacotron.py b/TTS/tts/models/base_tacotron.py index a537afd6..8cfd750d 100644 --- a/TTS/tts/models/base_tacotron.py +++ b/TTS/tts/models/base_tacotron.py @@ -115,11 +115,16 @@ class BaseTacotron(BaseTTS): ): # pylint: disable=unused-argument, redefined-builtin state = load_fsspec(checkpoint_path, map_location=torch.device("cpu")) self.load_state_dict(state["model"]) + # TODO: set r in run-time by taking it from the new config if "r" in state: + # set r from the state (for compatibility with older checkpoints) self.decoder.set_r(state["r"]) - else: - # set the reduction rate from the config values embedded in the checkpoint + elif "config" in state: + # set r from config used at training time (for inference) self.decoder.set_r(state["config"]["r"]) + else: + # set r from the new config (for new-models) + self.decoder.set_r(config.r) if eval: self.eval() print(f" > Model's reduction rate `r` is set to: {self.decoder.r}") From 7d8f77385a8cc55f770791fdb3e03f6df437ee18 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:27:33 +0000 Subject: [PATCH 23/33] Use `glow-tts` in synthesis tests --- TTS/utils/audio.py | 2 +- tests/inference_tests/test_synthesize.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/TTS/utils/audio.py b/TTS/utils/audio.py index 45c061d2..f5fb1d7f 100644 --- a/TTS/utils/audio.py +++ b/TTS/utils/audio.py @@ -610,7 +610,7 @@ class AudioProcessor(object): y = self._istft(S_complex * angles) if not np.isfinite(y).all(): print(" [!] Waveform is not finite everywhere. Skipping the GL.") - return np.array([0]) + return np.array([0.0]) for _ in range(self.griffin_lim_iters): angles = np.exp(1j * np.angle(self._stft(y))) y = self._istft(S_complex * angles) diff --git a/tests/inference_tests/test_synthesize.py b/tests/inference_tests/test_synthesize.py index 526f7dc8..635506ab 100644 --- a/tests/inference_tests/test_synthesize.py +++ b/tests/inference_tests/test_synthesize.py @@ -11,11 +11,10 @@ def test_synthesize(): # single speaker model run_cli(f'tts --text "This is an example." --out_path "{output_path}"') run_cli( - "tts --model_name tts_models/en/ljspeech/speedy-speech-wn " - f'--text "This is an example." --out_path "{output_path}"' + "tts --model_name tts_models/en/ljspeech/glow-tts " f'--text "This is an example." --out_path "{output_path}"' ) run_cli( - "tts --model_name tts_models/en/ljspeech/speedy-speech-wn " + "tts --model_name tts_models/en/ljspeech/glow-tts " "--vocoder_name vocoder_models/en/ljspeech/multiband-melgan " f'--text "This is an example." --out_path "{output_path}"' ) From 1ebf9ec6bf8fbf8f3a40ab7a4c5209d611bd8e37 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:28:20 +0000 Subject: [PATCH 24/33] Remove speedy_speech implementation --- tests/tts_tests/test_fast_pitch.py | 47 ---------- tests/tts_tests/test_speedy_speech_layers.py | 96 -------------------- tests/tts_tests/test_speedy_speech_train.py | 2 - 3 files changed, 145 deletions(-) delete mode 100644 tests/tts_tests/test_fast_pitch.py delete mode 100644 tests/tts_tests/test_speedy_speech_layers.py diff --git a/tests/tts_tests/test_fast_pitch.py b/tests/tts_tests/test_fast_pitch.py deleted file mode 100644 index 1975435e..00000000 --- a/tests/tts_tests/test_fast_pitch.py +++ /dev/null @@ -1,47 +0,0 @@ -import unittest - -import torch as T - -from TTS.tts.models.fast_pitch import FastPitch, FastPitchArgs, average_pitch -# pylint: disable=unused-variable - - -class AveragePitchTests(unittest.TestCase): - def test_in_out(self): # pylint: disable=no-self-use - pitch = T.rand(1, 1, 128) - - durations = T.randint(1, 5, (1, 21)) - coeff = 128.0 / durations.sum() - durations = T.round(durations * coeff) - diff = 128.0 - durations.sum() - durations[0, -1] += diff - durations = durations.long() - - pitch_avg = average_pitch(pitch, durations) - - index = 0 - for idx, dur in enumerate(durations[0]): - assert abs(pitch_avg[0, 0, idx] - pitch[0, 0, index : index + dur.item()].mean()) < 1e-5 - index += dur - - -def expand_encoder_outputs_test(): - model = FastPitch(FastPitchArgs(num_chars=10)) - - inputs = T.rand(2, 5, 57) - durations = T.randint(1, 4, (2, 57)) - - x_mask = T.ones(2, 1, 57) - y_mask = T.ones(2, 1, durations.sum(1).max()) - - expanded, _ = model.expand_encoder_outputs(inputs, durations, x_mask, y_mask) - - for b in range(durations.shape[0]): - index = 0 - for idx, dur in enumerate(durations[b]): - diff = ( - expanded[b, :, index : index + dur.item()] - - inputs[b, :, idx].repeat(dur.item()).view(expanded[b, :, index : index + dur.item()].shape) - ).sum() - assert abs(diff) < 1e-6, diff - index += dur diff --git a/tests/tts_tests/test_speedy_speech_layers.py b/tests/tts_tests/test_speedy_speech_layers.py deleted file mode 100644 index adf17b8b..00000000 --- a/tests/tts_tests/test_speedy_speech_layers.py +++ /dev/null @@ -1,96 +0,0 @@ -import torch - -from TTS.tts.configs import SpeedySpeechConfig -from TTS.tts.layers.feed_forward.duration_predictor import DurationPredictor -from TTS.tts.models.speedy_speech import SpeedySpeech, SpeedySpeechArgs -from TTS.tts.utils.helpers import sequence_mask - -use_cuda = torch.cuda.is_available() -device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - -def test_duration_predictor(): - input_dummy = torch.rand(8, 128, 27).to(device) - input_lengths = torch.randint(20, 27, (8,)).long().to(device) - input_lengths[-1] = 27 - - x_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device) - - layer = DurationPredictor(hidden_channels=128).to(device) - - output = layer(input_dummy, x_mask) - assert list(output.shape) == [8, 1, 27] - - -def test_speedy_speech(): - num_chars = 7 - B = 8 - T_en = 37 - T_de = 74 - - x_dummy = torch.randint(0, 7, (B, T_en)).long().to(device) - x_lengths = torch.randint(31, T_en, (B,)).long().to(device) - x_lengths[-1] = T_en - - # set durations. max total duration should be equal to T_de - durations = torch.randint(1, 4, (B, T_en)) - durations = durations * (T_de / durations.sum(1)).unsqueeze(1) - durations = durations.to(torch.long).to(device) - max_dur = durations.sum(1).max() - durations[:, 0] += T_de - max_dur if T_de > max_dur else 0 - - y_lengths = durations.sum(1) - - config = SpeedySpeechConfig(model_args=SpeedySpeechArgs(num_chars=num_chars, out_channels=80, hidden_channels=128)) - model = SpeedySpeech(config) - if use_cuda: - model.cuda() - - # forward pass - outputs = model(x_dummy, x_lengths, y_lengths, durations) - o_de = outputs["model_outputs"] - attn = outputs["alignments"] - o_dr = outputs["durations_log"] - - assert list(o_de.shape) == [B, T_de, 80], f"{list(o_de.shape)}" - assert list(attn.shape) == [B, T_de, T_en] - assert list(o_dr.shape) == [B, T_en] - - # with speaker embedding - config = SpeedySpeechConfig( - model_args=SpeedySpeechArgs( - num_chars=num_chars, out_channels=80, hidden_channels=128, num_speakers=80, d_vector_dim=256 - ) - ) - model = SpeedySpeech(config).to(device) - model.forward( - x_dummy, x_lengths, y_lengths, durations, aux_input={"d_vectors": torch.randint(0, 10, (B,)).to(device)} - ) - o_de = outputs["model_outputs"] - attn = outputs["alignments"] - o_dr = outputs["durations_log"] - - assert list(o_de.shape) == [B, T_de, 80], f"{list(o_de.shape)}" - assert list(attn.shape) == [B, T_de, T_en] - assert list(o_dr.shape) == [B, T_en] - - # with speaker external embedding - config = SpeedySpeechConfig( - model_args=SpeedySpeechArgs( - num_chars=num_chars, - out_channels=80, - hidden_channels=128, - num_speakers=10, - use_d_vector=True, - d_vector_dim=256, - ) - ) - model = SpeedySpeech(config).to(device) - model.forward(x_dummy, x_lengths, y_lengths, durations, aux_input={"d_vectors": torch.rand((B, 256)).to(device)}) - o_de = outputs["model_outputs"] - attn = outputs["alignments"] - o_dr = outputs["durations_log"] - - assert list(o_de.shape) == [B, T_de, 80], f"{list(o_de.shape)}" - assert list(attn.shape) == [B, T_de, T_en] - assert list(o_dr.shape) == [B, T_en] diff --git a/tests/tts_tests/test_speedy_speech_train.py b/tests/tts_tests/test_speedy_speech_train.py index 28dc7029..a181ac24 100644 --- a/tests/tts_tests/test_speedy_speech_train.py +++ b/tests/tts_tests/test_speedy_speech_train.py @@ -4,14 +4,12 @@ import shutil from tests import get_device_id, get_tests_output_path, run_cli from TTS.tts.configs import SpeedySpeechConfig -from TTS.tts.models.speedy_speech import SpeedySpeechArgs config_path = os.path.join(get_tests_output_path(), "test_speedy_speech_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") config = SpeedySpeechConfig( - model_args=SpeedySpeechArgs(num_chars=50, out_channels=80, hidden_channels=128, num_speakers=0), batch_size=8, eval_batch_size=8, num_loader_workers=0, From 7ec23e69d4ab6528d6dffc34aeb1e8668a11cc23 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:28:58 +0000 Subject: [PATCH 25/33] Skip TF tests on GPU --- tests/tts_tests/test_tacotron2_tf_model.py | 3 +++ tests/vocoder_tests/test_vocoder_tf_melgan_generator.py | 6 ++++++ tests/vocoder_tests/test_vocoder_tf_pqmf.py | 4 ++++ 3 files changed, 13 insertions(+) diff --git a/tests/tts_tests/test_tacotron2_tf_model.py b/tests/tts_tests/test_tacotron2_tf_model.py index 431b0c2f..515a6834 100644 --- a/tests/tts_tests/test_tacotron2_tf_model.py +++ b/tests/tts_tests/test_tacotron2_tf_model.py @@ -38,6 +38,7 @@ class TacotronTFTrainTest(unittest.TestCase): mel_spec = tf.convert_to_tensor(mel_spec.cpu().numpy()) return chars_seq, chars_seq_lengths, mel_spec, mel_postnet_spec, mel_lengths, stop_targets, speaker_ids + @unittest.skipIf(use_cuda, " [!] Skip Test: TfLite conversion does not work on GPU.") def test_train_step(self): """test forward pass""" ( @@ -70,6 +71,7 @@ class TacotronTFTrainTest(unittest.TestCase): # inference pass output = model(chars_seq, training=False) + @unittest.skipIf(use_cuda, " [!] Skip Test: TfLite conversion does not work on GPU.") def test_forward_attention( self, ): @@ -103,6 +105,7 @@ class TacotronTFTrainTest(unittest.TestCase): # inference pass output = model(chars_seq, training=False) + @unittest.skipIf(use_cuda, " [!] Skip Test: TfLite conversion does not work on GPU.") def test_tflite_conversion( self, ): # pylint:disable=no-self-use diff --git a/tests/vocoder_tests/test_vocoder_tf_melgan_generator.py b/tests/vocoder_tests/test_vocoder_tf_melgan_generator.py index 67968225..225ceaf5 100644 --- a/tests/vocoder_tests/test_vocoder_tf_melgan_generator.py +++ b/tests/vocoder_tests/test_vocoder_tf_melgan_generator.py @@ -1,9 +1,15 @@ +import unittest + import numpy as np import tensorflow as tf +import torch from TTS.vocoder.tf.models.melgan_generator import MelganGenerator +use_cuda = torch.cuda.is_available() + +@unittest.skipIf(use_cuda, " [!] Skip Test: Loosy TF support.") def test_melgan_generator(): hop_length = 256 model = MelganGenerator() diff --git a/tests/vocoder_tests/test_vocoder_tf_pqmf.py b/tests/vocoder_tests/test_vocoder_tf_pqmf.py index f1c3666b..6acb20d9 100644 --- a/tests/vocoder_tests/test_vocoder_tf_pqmf.py +++ b/tests/vocoder_tests/test_vocoder_tf_pqmf.py @@ -1,7 +1,9 @@ import os +import unittest import soundfile as sf import tensorflow as tf +import torch from librosa.core import load from tests import get_tests_input_path, get_tests_output_path, get_tests_path @@ -9,8 +11,10 @@ from TTS.vocoder.tf.layers.pqmf import PQMF TESTS_PATH = get_tests_path() WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav") +use_cuda = torch.cuda.is_available() +@unittest.skipIf(use_cuda, " [!] Skip Test: Loosy TF support.") def test_pqmf(): w, sr = load(WAV_FILE) From d97952611d33e80721e1b96c0139b31c506bd73f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:31:41 +0000 Subject: [PATCH 26/33] Remove unused import --- TTS/tts/models/base_tts.py | 1 - 1 file changed, 1 deletion(-) diff --git a/TTS/tts/models/base_tts.py b/TTS/tts/models/base_tts.py index 90ac18e9..06c7cb2b 100644 --- a/TTS/tts/models/base_tts.py +++ b/TTS/tts/models/base_tts.py @@ -1,4 +1,3 @@ -from TTS.tts.utils.helpers import segment import os from typing import Dict, List, Tuple From 26f76fce22d8d9b9423fa2a722aa36e628d45af0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Fri, 10 Sep 2021 17:47:27 +0000 Subject: [PATCH 27/33] Remove SpeedySpeech from .models.json --- TTS/.models.json | 9 --------- TTS/tts/layers/__init__.py | 14 -------------- TTS/tts/utils/data.py | 1 - TTS/tts/utils/helpers.py | 6 +++--- tests/tts_tests/test_forward_tts.py | 26 ++++++++++++-------------- tests/tts_tests/test_helpers.py | 4 ++-- 6 files changed, 17 insertions(+), 43 deletions(-) diff --git a/TTS/.models.json b/TTS/.models.json index 6f763840..aae2a1c2 100644 --- a/TTS/.models.json +++ b/TTS/.models.json @@ -47,15 +47,6 @@ "license": "MPL", "contact": "egolge@coqui.com" }, - "speedy-speech-wn": { - "description": "Speedy Speech model with wavenet decoder.", - "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.1.0/tts_models--en--ljspeech--speedy-speech-wn.zip", - "default_vocoder": "vocoder_models/en/ljspeech/multiband-melgan", - "commit": "77b6145", - "author": "Eren Gölge @erogol", - "license": "MPL", - "contact": "egolge@coqui.com" - }, "vits": { "description": "VITS is an End2End TTS model trained on LJSpeech dataset with phonemes.", "github_rls_url": "https://github.com/coqui-ai/TTS/releases/download/v0.2.0/tts_models--en--ljspeech--vits.zip", diff --git a/TTS/tts/layers/__init__.py b/TTS/tts/layers/__init__.py index 78f56a5d..f93efdb7 100644 --- a/TTS/tts/layers/__init__.py +++ b/TTS/tts/layers/__init__.py @@ -1,15 +1 @@ from TTS.tts.layers.losses import * - - -def setup_loss(config): - if config.model.lower() in ["tacotron", "tacotron2"]: - model = TacotronLoss(config) - elif config.model.lower() == "glow_tts": - model = GlowTTSLoss() - elif config.model.lower() == "speedy_speech": - model = SpeedySpeechLoss(config) - elif config.model.lower() == "align_tts": - model = AlignTTSLoss(config) - else: - raise ValueError(f" [!] loss for model {config.model.lower()} cannot be found.") - return model diff --git a/TTS/tts/utils/data.py b/TTS/tts/utils/data.py index d91a828e..b0d88740 100644 --- a/TTS/tts/utils/data.py +++ b/TTS/tts/utils/data.py @@ -1,5 +1,4 @@ import numpy as np -import torch def _pad_data(x, length): diff --git a/TTS/tts/utils/helpers.py b/TTS/tts/utils/helpers.py index 76abf2bc..b0a010b0 100644 --- a/TTS/tts/utils/helpers.py +++ b/TTS/tts/utils/helpers.py @@ -11,11 +11,11 @@ except ModuleNotFoundError: class StandardScaler: - """StandardScaler for mean-std normalization with the given mean and std values.""" + """StandardScaler for mean-scale normalization with the given mean and scale values.""" - def __init__(self, mean: np.ndarray = None, std: np.ndarray = None) -> None: + def __init__(self, mean: np.ndarray = None, scale: np.ndarray = None) -> None: self.mean_ = mean - self.std_ = std + self.scale_ = scale def set_stats(self, mean, scale): self.mean_ = mean diff --git a/tests/tts_tests/test_forward_tts.py b/tests/tts_tests/test_forward_tts.py index 9bb60f48..cec0f211 100644 --- a/tests/tts_tests/test_forward_tts.py +++ b/tests/tts_tests/test_forward_tts.py @@ -1,5 +1,3 @@ -import unittest - import torch as T from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs @@ -54,12 +52,12 @@ def model_input_output_test(): assert (outputs["x_mask"] - x_mask).sum() == 0.0 assert (outputs["y_mask"] - y_mask).sum() == 0.0 - assert outputs["alignment_soft"] == None - assert outputs["alignment_mas"] == None - assert outputs["alignment_logprob"] == None - assert outputs["o_alignment_dur"] == None - assert outputs["pitch_avg"] == None - assert outputs["pitch_avg_gt"] == None + assert outputs["alignment_soft"] is None + assert outputs["alignment_mas"] is None + assert outputs["alignment_logprob"] is None + assert outputs["o_alignment_dur"] is None + assert outputs["pitch_avg"] is None + assert outputs["pitch_avg_gt"] is None # USE PITCH model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False)) @@ -85,10 +83,10 @@ def model_input_output_test(): assert outputs["pitch_avg"].shape == (2, 1, 21) assert outputs["pitch_avg_gt"].shape == (2, 1, 21) - assert outputs["alignment_soft"] == None - assert outputs["alignment_mas"] == None - assert outputs["alignment_logprob"] == None - assert outputs["o_alignment_dur"] == None + assert outputs["alignment_soft"] is None + assert outputs["alignment_mas"] is None + assert outputs["alignment_logprob"] is None + assert outputs["o_alignment_dur"] is None # USE ALIGNER NETWORK model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True)) @@ -116,8 +114,8 @@ def model_input_output_test(): assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21) assert outputs["o_alignment_dur"].shape == (2, 21) - assert outputs["pitch_avg"] == None - assert outputs["pitch_avg_gt"] == None + assert outputs["pitch_avg"] is None + assert outputs["pitch_avg_gt"] is None # USE ALIGNER NETWORK AND PITCH model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True)) diff --git a/tests/tts_tests/test_helpers.py b/tests/tts_tests/test_helpers.py index 0aac5473..6a2f260d 100644 --- a/tests/tts_tests/test_helpers.py +++ b/tests/tts_tests/test_helpers.py @@ -1,6 +1,6 @@ import torch as T -from TTS.tts.utils.helpers import * +from TTS.tts.utils.helpers import average_over_durations, generate_path, segment, sequence_mask def average_over_durations_test(): # pylint: disable=no-self-use @@ -47,7 +47,7 @@ def generate_path_test(): durations = durations * x_mask.squeeze(1) y_length = durations.sum(1) y_mask = sequence_mask(y_length).unsqueeze(1).long() - attn_mask = (torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)).squeeze(1).long() + attn_mask = (T.unsqueeze(x_mask, -1) * T.unsqueeze(y_mask, 2)).squeeze(1).long() print(attn_mask.shape) path = generate_path(durations, attn_mask) assert path.shape == (10, 21, durations.sum(1).max().item()) From 1e7db32e90eaafa4bcafe5512f6f1b9d1e5f32f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Sat, 11 Sep 2021 10:19:47 +0000 Subject: [PATCH 28/33] Test FastPitch train --- tests/tts_tests/test_fast_pitch_train.py | 68 ++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 tests/tts_tests/test_fast_pitch_train.py diff --git a/tests/tts_tests/test_fast_pitch_train.py b/tests/tts_tests/test_fast_pitch_train.py new file mode 100644 index 00000000..89176ac9 --- /dev/null +++ b/tests/tts_tests/test_fast_pitch_train.py @@ -0,0 +1,68 @@ +import glob +import os +import shutil + +from tests import get_device_id, get_tests_output_path, run_cli +from TTS.config.shared_configs import BaseAudioConfig +from TTS.tts.configs import FastPitchConfig + +config_path = os.path.join(get_tests_output_path(), "test_fast_pitch_config.json") +output_path = os.path.join(get_tests_output_path(), "train_outputs") + +audio_config = BaseAudioConfig( + sample_rate=22050, + do_trim_silence=True, + trim_db=60.0, + signal_norm=False, + mel_fmin=0.0, + mel_fmax=8000, + spec_gain=1.0, + log_func="np.log", + ref_level_db=20, + preemphasis=0.0, +) + +config = FastPitchConfig( + audio=audio_config, + batch_size=8, + eval_batch_size=8, + num_loader_workers=0, + num_eval_loader_workers=0, + text_cleaner="english_cleaners", + use_phonemes=True, + phoneme_language="en-us", + phoneme_cache_path="tests/data/ljspeech/phoneme_cache/", + f0_cache_path="tests/data/ljspeech/f0_cache/", + run_eval=True, + test_delay_epochs=-1, + epochs=1, + print_step=1, + print_eval=True, + test_sentences=[ + "Be a voice, not an echo.", + ], +) +config.audio.do_trim_silence = True +config.audio.trim_db = 60 +config.save_json(config_path) + +# train the model for one epoch +command_train = ( + f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} " + f"--coqpit.output_path {output_path} " + "--coqpit.datasets.0.name ljspeech " + "--coqpit.datasets.0.meta_file_train metadata.csv " + "--coqpit.datasets.0.meta_file_val metadata.csv " + "--coqpit.datasets.0.path tests/data/ljspeech " + "--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt " + "--coqpit.test_delay_epochs 0" +) +run_cli(command_train) + +# Find latest folder +continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime) + +# restore the model and continue training for one more epoch +command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} " +run_cli(command_train) +shutil.rmtree(continue_path) From cbbc9e017278155af3dac3dc33d13dda400c18d2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Sat, 11 Sep 2021 10:20:37 +0000 Subject: [PATCH 29/33] Add FastSpeechConfig --- TTS/tts/configs/fast_speech_config.py | 151 ++++++++++++++++++++++++ TTS/tts/configs/speedy_speech_config.py | 5 +- TTS/tts/models/forward_tts.py | 17 ++- 3 files changed, 165 insertions(+), 8 deletions(-) create mode 100644 TTS/tts/configs/fast_speech_config.py diff --git a/TTS/tts/configs/fast_speech_config.py b/TTS/tts/configs/fast_speech_config.py new file mode 100644 index 00000000..bba47bb3 --- /dev/null +++ b/TTS/tts/configs/fast_speech_config.py @@ -0,0 +1,151 @@ +from dataclasses import dataclass, field +from typing import List + +from TTS.tts.configs.shared_configs import BaseTTSConfig +from TTS.tts.models.forward_tts import ForwardTTSArgs + + +@dataclass +class FastSpeechConfig(BaseTTSConfig): + """Configure `ForwardTTS` as FastSpeech model. + + Example: + + >>> from TTS.tts.configs import FastSpeechConfig + >>> config = FastSpeechConfig() + + Args: + model (str): + Model name used for selecting the right model at initialization. Defaults to `fast_pitch`. + + base_model (str): + Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate + the base model rather than searching for the `model` implementation. Defaults to `forward_tts`. + + model_args (Coqpit): + Model class arguments. Check `FastSpeechArgs` for more details. Defaults to `FastSpeechArgs()`. + + data_dep_init_steps (int): + Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses + Activation Normalization that pre-computes normalization stats at the beginning and use the same values + for the rest. Defaults to 10. + + use_speaker_embedding (bool): + enable / disable using speaker embeddings for multi-speaker models. If set True, the model is + in the multi-speaker mode. Defaults to False. + + use_d_vector_file (bool): + enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False. + + d_vector_file (str): + Path to the file including pre-computed speaker embeddings. Defaults to None. + + d_vector_dim (int): + Dimension of the external speaker embeddings. Defaults to 0. + + optimizer (str): + Name of the model optimizer. Defaults to `Adam`. + + optimizer_params (dict): + Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`. + + lr_scheduler (str): + Name of the learning rate scheduler. Defaults to `Noam`. + + lr_scheduler_params (dict): + Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`. + + lr (float): + Initial learning rate. Defaults to `1e-3`. + + grad_clip (float): + Gradient norm clipping value. Defaults to `5.0`. + + spec_loss_type (str): + Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`. + + duration_loss_type (str): + Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`. + + use_ssim_loss (bool): + Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True. + + wd (float): + Weight decay coefficient. Defaults to `1e-7`. + + ssim_loss_alpha (float): + Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0. + + dur_loss_alpha (float): + Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0. + + spec_loss_alpha (float): + Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0. + + pitch_loss_alpha (float): + Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0. + + binary_loss_alpha (float): + Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0. + + binary_align_loss_start_step (int): + Start binary alignment loss after this many steps. Defaults to 20000. + + min_seq_len (int): + Minimum input sequence length to be used at training. + + max_seq_len (int): + Maximum input sequence length to be used at training. Larger values result in more VRAM usage. + """ + + model: str = "fast_speech" + base_model: str = "forward_tts" + + # model specific params + model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=False) + + # multi-speaker settings + use_speaker_embedding: bool = False + use_d_vector_file: bool = False + d_vector_file: str = False + d_vector_dim: int = 0 + + # optimizer parameters + optimizer: str = "Adam" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6}) + lr_scheduler: str = "NoamLR" + lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000}) + lr: float = 1e-4 + grad_clip: float = 5.0 + + # loss params + spec_loss_type: str = "mse" + duration_loss_type: str = "mse" + use_ssim_loss: bool = True + ssim_loss_alpha: float = 1.0 + dur_loss_alpha: float = 1.0 + spec_loss_alpha: float = 1.0 + pitch_loss_alpha: float = 0.0 + aligner_loss_alpha: float = 1.0 + binary_align_loss_alpha: float = 1.0 + binary_align_loss_start_step: int = 20000 + + # overrides + min_seq_len: int = 13 + max_seq_len: int = 200 + r: int = 1 # DO NOT CHANGE + + # dataset configs + compute_f0: bool = True + f0_cache_path: str = None + + # testing + test_sentences: List[str] = field( + default_factory=lambda: [ + "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", + "Be a voice, not an echo.", + "I'm sorry Dave. I'm afraid I can't do that.", + "This cake is great. It's so delicious and moist.", + "Prior to November 22, 1963.", + ] + ) diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py index 1ec8f729..23a96ff1 100644 --- a/TTS/tts/configs/speedy_speech_config.py +++ b/TTS/tts/configs/speedy_speech_config.py @@ -119,6 +119,7 @@ class SpeedySpeechConfig(BaseTTSConfig): hidden_channels=128, num_speakers=0, positional_encoding=True, + detach_duration_predictor=True ) # multi-speaker settings @@ -128,7 +129,7 @@ class SpeedySpeechConfig(BaseTTSConfig): d_vector_dim: int = 0 # optimizer parameters - optimizer: str = "RAdam" + optimizer: str = "Adam" optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6}) lr_scheduler: str = "NoamLR" lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000}) @@ -138,7 +139,7 @@ class SpeedySpeechConfig(BaseTTSConfig): # loss params spec_loss_type: str = "l1" duration_loss_type: str = "huber" - use_ssim_loss: bool = True + use_ssim_loss: bool = False ssim_loss_alpha: float = 1.0 dur_loss_alpha: float = 1.0 spec_loss_alpha: float = 1.0 diff --git a/TTS/tts/models/forward_tts.py b/TTS/tts/models/forward_tts.py index c4411027..9dce36fa 100644 --- a/TTS/tts/models/forward_tts.py +++ b/TTS/tts/models/forward_tts.py @@ -99,6 +99,7 @@ class ForwardTTSArgs(Coqpit): max_duration (int): Maximum duration accepted by the model. Defaults to 75. + """ num_chars: int = None @@ -264,18 +265,22 @@ class ForwardTTS(BaseTTS): """Generate attention alignment map from durations and expand encoder outputs - Shapes + Shapes: - en: :math:`(B, D_{en}, T_{en})` - dr: :math:`(B, T_{en})` - x_mask: :math:`(B, T_{en})` - y_mask: :math:`(B, T_{de})` - Examples: - - encoder output: :math:`[a,b,c,d]` - - durations: :math:`[1, 3, 2, 1]` + Examples:: - - expanded: :math:`[a, b, b, b, c, c, d]` - - attention map: :math:`[[0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]]` + encoder output: [a,b,c,d] + durations: [1, 3, 2, 1] + + expanded: [a, b, b, b, c, c, d] + attention map: [[0, 0, 0, 0, 0, 0, 1], + [0, 0, 0, 0, 1, 1, 0], + [0, 1, 1, 1, 0, 0, 0], + [1, 0, 0, 0, 0, 0, 0]] """ attn = self.generate_attn(dr, x_mask, y_mask) o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2).to(en.dtype), en.transpose(1, 2)).transpose(1, 2) From bb69e714d044c98928fb35f445b0d9d44e707bc3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Sun, 12 Sep 2021 15:32:53 +0000 Subject: [PATCH 30/33] Add `docs` to Makefile --- Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 3e33ca88..bffed7ff 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ .DEFAULT_GOAL := help -.PHONY: test system-deps dev-deps deps style lint install help +.PHONY: test system-deps dev-deps deps style lint install help docs help: @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' @@ -45,3 +45,6 @@ deps: ## install 🐸 requirements. install: ## install 🐸 TTS for development. pip install -e .[all] + +docs: ## build the docs + $(MAKE) -C docs clean && $(MAKE) -C docs html From 1ea011571abfa37c9b8d725a4efe1e0831509de5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Sun, 12 Sep 2021 15:33:27 +0000 Subject: [PATCH 31/33] Update SpeedySpeech config --- README.md | 1 + TTS/tts/configs/speedy_speech_config.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 18d1469c..577eb3e9 100644 --- a/README.md +++ b/README.md @@ -73,6 +73,7 @@ Underlined "TTS*" and "Judy*" are 🐸TTS models - Speedy-Speech: [paper](https://arxiv.org/abs/2008.03802) - Align-TTS: [paper](https://arxiv.org/abs/2003.01950) - FastPitch: [paper](https://arxiv.org/pdf/2006.06873.pdf) +- FastSpeech: [paper](https://arxiv.org/abs/1905.09263) ### End-to-End Models - VITS: [paper](https://arxiv.org/pdf/2106.06103) diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py index 23a96ff1..7c9bb8e4 100644 --- a/TTS/tts/configs/speedy_speech_config.py +++ b/TTS/tts/configs/speedy_speech_config.py @@ -144,8 +144,8 @@ class SpeedySpeechConfig(BaseTTSConfig): dur_loss_alpha: float = 1.0 spec_loss_alpha: float = 1.0 aligner_loss_alpha: float = 1.0 - binary_align_loss_alpha: float = 1.0 - binary_align_loss_start_step: int = 20000 + binary_align_loss_alpha: float = 0.3 + binary_align_loss_start_step: int = 50000 # overrides min_seq_len: int = 13 From edc8d4d83396a389bef4caa67a16b9e83d6e62c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Sun, 12 Sep 2021 15:34:27 +0000 Subject: [PATCH 32/33] Add `forward_tts` docs --- docs/source/index.md | 2 +- docs/source/models/fast_pitch.md | 28 ------------- docs/source/models/forward_tts.md | 65 +++++++++++++++++++++++++++++++ 3 files changed, 66 insertions(+), 29 deletions(-) delete mode 100644 docs/source/models/fast_pitch.md create mode 100644 docs/source/models/forward_tts.md diff --git a/docs/source/index.md b/docs/source/index.md index d842f894..fab14d12 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -45,7 +45,7 @@ models/glow_tts.md models/vits.md - models/fast_pitch.md + models/forward_tts.md .. toctree:: :maxdepth: 2 diff --git a/docs/source/models/fast_pitch.md b/docs/source/models/fast_pitch.md deleted file mode 100644 index 75ab0812..00000000 --- a/docs/source/models/fast_pitch.md +++ /dev/null @@ -1,28 +0,0 @@ -# FastPitch - -FastPitch is a feed-forward encoder-decoder TTS model. It computes mel-spectrogram from the given input character sequence. -It uses a duration predictor network to predict the duration of each input character in the output sequence. In the original paper, they use a pre-trained Tacotron model to generate the labels for the duration predictor. In this implementation, you can also use an aligner network to learn the durations from the data and train the duration predictor in parallel. Original FastPitch model uses FeedForwardTransformer networks for both encoder and decoder. But in this implementation, you have the freedom to choose different encoder and decoder networks by just changing the relevant fields in the model configuration. Please see `FastPitchArgs` and `FastPitchConfig` below for more details. - -## Important resources & papers -- FastPitch: https://arxiv.org/abs/2006.06873 -- FastSpeech: https://arxiv.org/pdf/1905.09263 -- Aligner Network: https://arxiv.org/abs/2108.10447 -- What is Pitch: https://www.britannica.com/topic/pitch-speech - -## FastPitchConfig -```{eval-rst} -.. autoclass:: TTS.tts.configs.fast_pitch_config.FastPitchConfig - :members: -``` - -## FastPitchArgs -```{eval-rst} -.. autoclass:: TTS.tts.models.fast_pitch.FastPitchArgs - :members: -``` - -## FastPitch Model -```{eval-rst} -.. autoclass:: TTS.tts.models.fast_pitch.FastPitch - :members: -``` diff --git a/docs/source/models/forward_tts.md b/docs/source/models/forward_tts.md new file mode 100644 index 00000000..c4affb69 --- /dev/null +++ b/docs/source/models/forward_tts.md @@ -0,0 +1,65 @@ +# Forward TTS model(s) + +A general feed-forward TTS model implementation that can be configured to different architectures by setting different +encoder and decoder networks. It can be trained with either pre-computed durations (from pre-trained Tacotron) or +an alignment network that learns the text to audio alignment from the input data. + +Currently we provide the following pre-configured architectures: + +- **FastSpeech:** + + It's a feed-forward model TTS model that uses Feed Forward Transformer (FFT) modules as the encoder and decoder. + +- **FastPitch:** + + It uses the same FastSpeech architecture that us conditioned on fundemental frequency (f0) contours with the + promise of more expressive speech. + +- **SpeedySpeech:** + + It uses Residual Convolution layers instead of Transformers that leads to a more compute friendly model. + +- **FastSpeech2 (TODO):** + + Similar to FastPitch but it also uses a spectral energy values as an addition. + +## Important resources & papers +- FastPitch: https://arxiv.org/abs/2006.06873 +- SpeedySpeech: https://arxiv.org/abs/2008.03802 +- FastSpeech: https://arxiv.org/pdf/1905.09263 +- FastSpeech2: https://arxiv.org/abs/2006.04558 +- Aligner Network: https://arxiv.org/abs/2108.10447 +- What is Pitch: https://www.britannica.com/topic/pitch-speech + + +## ForwardTTSArgs +```{eval-rst} +.. autoclass:: TTS.tts.models.forward_tts.ForwardTTSArgs + :members: +``` + +## ForwardTTS Model +```{eval-rst} +.. autoclass:: TTS.tts.models.forward_tts.ForwardTTS + :members: +``` + +## FastPitchConfig +```{eval-rst} +.. autoclass:: TTS.tts.configs.fast_pitch_config.FastPitchConfig + :members: +``` + +## SpeedySpeechConfig +```{eval-rst} +.. autoclass:: TTS.tts.configs.speedy_speech_config.SpeedySpeechConfig + :members: +``` + +## FastSpeechConfig +```{eval-rst} +.. autoclass:: TTS.tts.configs.fast_speech_config.FastSpeechConfig + :members: +``` + + From 69dd36ee3b6ce5974ad6d4b8feeb57c1a03a45ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Sun, 12 Sep 2021 20:34:19 +0000 Subject: [PATCH 33/33] Add fine-tunning documentation --- docs/source/finetuning.md | 115 +++++++++++++++++++++++++ docs/source/formatting_your_dataset.md | 11 +-- docs/source/index.md | 1 + 3 files changed, 122 insertions(+), 5 deletions(-) create mode 100644 docs/source/finetuning.md diff --git a/docs/source/finetuning.md b/docs/source/finetuning.md new file mode 100644 index 00000000..35cd6f19 --- /dev/null +++ b/docs/source/finetuning.md @@ -0,0 +1,115 @@ +# Fine-tuning a 🐸 TTS model + +## Fine-tuning + +Fine-tuning takes a pre-trained model, and retrains it to improve the model performance on a different task or dataset. +In 🐸TTS we provide different pre-trained models in different languages and different pros and cons. You can take one of +them and fine-tune it for your own dataset. This will help you in two main ways: + +1. Faster learning + + Since a pre-trained model has already learned features that are relevant for the task, it will converge faster on + a new dataset. This will reduce the cost of training and let you experient faster. + +2. Better resutls with small datasets + + Deep learning models are data hungry and they give better performance with more data. However, it is not always + possible to have this abondance, especially in domain. For instance, LJSpeech dataset, that we released most of + our English models with, is almost 24 hours long. And it requires for someone to collect thid amount of data with + a help of a voice talent takes weeks. + + Fine-tuning cames to rescue in this case. You can take one of our pre-trained models and fine-tune it for your own + speech dataset and achive reasonable results with only a couple of hours in the worse case. + + However, note that, fine-tuning does not promise great results. The model performance is still depends on the + {ref}`dataset quality ` and the hyper-parameters you choose for fine-tuning. Therefore, + it still demands a bit of tinkering. + + +## Steps to fine-tune a 🐸 TTS model + +1. Setup your dataset. + + You need to format your target dataset in a certain way so that 🐸TTS data loader would be able to load it for the + training. Please see {ref}`this page ` for more information about formatting. + +2. Choose the model you want to fine-tune. + + You can list the availabe models on terminal as + + ```bash + tts --list-models + ``` + + The command above lists the the models in a naming format as ```///```. + + Or you can manually check `.model.json` file in the project directory. + + You should choose the model based on your requirements. Some models are fast and some are better in speech quality. + One lazy way to check a model is running the model on the hardware you want to use and see how it works. For + simple testing, you can use the `tts` command on the terminal. For more info see {ref}`here `. + +3. Download the model. + + You can download the model by `tts` command. If you run `tts` with a particular model, it will download automatically + and the model path will be printed on the terminal. + + ```bash + tts --model_name tts_models/es/mai/tacotron2-DDC --text "Ola." + + > Downloading model to /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts + ... + ``` + + In the example above, we called the Spanish Tacotron model and give the sample output showing use the path where + the model is downloaded. + +4. Setup the model config for fine-tuning. + + You need to change certain fields in the model config. You have 3 options for playing with the configuration. + + 1. Edit the fields in the ```config.json``` file if you want to use ```TTS/bin/train_tts.py``` to train the model. + 2. Edit the fields in one of the training scripts in the ```recipes``` directory if you want to use python. + 3. Use the command-line arguments to override the fields like ```--coqpit.lr 0.00001``` to change the learning rate. + + Some of the important fields are as follows: + + - `datasets` field: This is set to the dataset you want to fine-tune the model on. + - `run_name` field: This is the name of the run. This is used to name the output directory and the entry in the + logging dashboard. + - `output_path` field: This is the path where the fine-tuned model is saved. + - `lr` field: You may need to use a smaller learning rate for fine-tuning not to impair the features learned by the + pre-trained model with big update steps. + - `audio` fields: Different datasets have different audio characteristics. You must check the current audio parameters and + make sure that the values reflect your dataset. For instance, your dataset might have a different audio sampling rate. + + Apart from these above, you should check the whole configuration file and make sure that the values are correct for + your dataset and training. + +5. Start fine-tuning. + + Whether you use one of the training scripts under ```recipes``` folder or the ```train_tts.py``` to start + your training, you should use the ```--restore_path``` flag to specify the path to the pre-trained model. + + ```bash + CUDA_VISIBLE_DEVICES="0" python recipes/ljspeech/glow_tts/train_glowtts.py \ + --restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts + ``` + + ```bash + CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py \ + --config_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts/config.json \ + --restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts + ``` + + As stated above, you can also use command-line arguments to change the model configuration. + + + ```bash + CUDA_VISIBLE_DEVICES="0" python recipes/ljspeech/glow_tts/train_glowtts.py \ + --restore_path /home/ubuntu/.local/share/tts/tts_models--en--ljspeech--glow-tts + --coqpit.run_name "glow-tts-finetune" \ + --coqpit.lr 0.00001 + ``` + + diff --git a/docs/source/formatting_your_dataset.md b/docs/source/formatting_your_dataset.md index cc0e456a..cbefc61d 100644 --- a/docs/source/formatting_your_dataset.md +++ b/docs/source/formatting_your_dataset.md @@ -1,3 +1,4 @@ +(formatting_your_dataset)= # Formatting Your Dataset For training a TTS model, you need a dataset with speech recordings and transcriptions. The speech must be divided into audio clips and each clip needs transcription. @@ -18,15 +19,15 @@ Let's assume you created the audio clips and their transcription. You can collec You can either create separate transcription files for each clip or create a text file that maps each audio clip to its transcription. In this file, each line must be delimitered by a special character separating the audio file name from the transcription. And make sure that the delimiter is not used in the transcription text. -We recommend the following format delimited by `|`. +We recommend the following format delimited by `||`. ``` # metadata.txt -audio1.wav | This is my sentence. -audio2.wav | This is maybe my sentence. -audio3.wav | This is certainly my sentence. -audio4.wav | Let this be your sentence. +audio1.wav || This is my sentence. +audio2.wav || This is maybe my sentence. +audio3.wav || This is certainly my sentence. +audio4.wav || Let this be your sentence. ... ``` diff --git a/docs/source/index.md b/docs/source/index.md index fab14d12..d5f77ad4 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -22,6 +22,7 @@ inference implementing_a_new_model training_a_model + finetuning configuration formatting_your_dataset what_makes_a_good_dataset